1e:I[23489,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js","/_next/static/immutable/chunks/2bmdwv49ba1qx.js","/_next/static/immutable/chunks/3ld61_1ghpsxn.js"],"ShareMenu"] 1f:I[55078,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js","/_next/static/immutable/chunks/2bmdwv49ba1qx.js","/_next/static/immutable/chunks/3ld61_1ghpsxn.js"],"AuthorMetricsPanel"] 20:I[39060,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js","/_next/static/immutable/chunks/2bmdwv49ba1qx.js","/_next/static/immutable/chunks/3ld61_1ghpsxn.js"],"AuthorPapers"] 34:I[49310,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js"],"IconMark"] 21:T72d,BACKGROUND: As part of an interdisciplinary study of medical injury and malpractice litigation, we estimated the incidence of adverse events, defined as injuries caused by medical management, and of the subgroup of such injuries that resulted from negligent or substandard care. METHODS: We reviewed 30,121 randomly selected records from 51 randomly selected acute care, nonpsychiatric hospitals in New York State in 1984. We then developed population estimates of injuries and computed rates according to the age and sex of the patients as well as the specialties of the physicians. RESULTS: Adverse events occurred in 3.7 percent of the hospitalizations (95 percent confidence interval, 3.2 to 4.2), and 27.6 percent of the adverse events were due to negligence (95 percent confidence interval, 22.5 to 32.6). Although 70.5 percent of the adverse events gave rise to disability lasting less than six months, 2.6 percent caused permanently disabling injuries and 13.6 percent led to death. The percentage of adverse events attributable to negligence increased in the categories of more severe injuries (Wald test chi 2 = 21.04, P less than 0.0001). Using weighted totals, we estimated that among the 2,671,863 patients discharged from New York hospitals in 1984 there were 98,609 adverse events and 27,179 adverse events involving negligence. Rates of adverse events rose with age (P less than 0.0001). The percentage of adverse events due to negligence was markedly higher among the elderly (P less than 0.01). There were significant differences in rates of adverse events among categories of clinical specialties (P less than 0.0001), but no differences in the percentage due to negligence. CONCLUSIONS: There is a substantial amount of injury to patients from medical management, and many injuries are the result of substandard care.22:T6c2,BACKGROUND: In a sample of 30,195 randomly selected hospital records, we identified 1133 patients (3.7 percent) with disabling injuries caused by medical treatment. We report here an analysis of these adverse events and their relation to error, negligence, and disability. METHODS: Two physician-reviewers independently identified the adverse events and evaluated them with respect to negligence, errors in management, and extent of disability. One of the authors classified each event according to type of injury. We tested the significance of differences in rates of negligence and disability among categories with at least 30 adverse events. RESULTS: Drug complications were the most common type of adverse event (19 percent), followed by wound infections (14 percent) and technical complications (13 percent). Nearly half the adverse events (48 percent) were associated with an operation. Adverse events during surgery were less likely to be caused by negligence (17 percent) than nonsurgical ones (37 percent). The proportion of adverse events due to negligence was highest for diagnostic mishaps (75 percent), noninvasive therapeutic mishaps ("errors of omission") (77 percent), and events occurring in the emergency room (70 percent). Errors in management were identified for 58 percent of the adverse events, among which nearly half were attributed to negligence. CONCLUSIONS: Although the prevention of many adverse events must await improvements in medical knowledge, the high proportion that are due to management errors suggests that many others are potentially preventable now. Reducing the incidence of these events will require identifying their causes and developing methods to prevent error or reduce its effects.23:T6ec,BACKGROUND: Overweight adults are at an increased risk of developing numerous chronic diseases. METHODS: Ten-year follow-up (1986-1996) of middle-aged women in the Nurses' Health Study and men in the Health Professionals Follow-up Study to assess the health risks associated with overweight. RESULTS: The risk of developing diabetes, gallstones, hypertension, heart disease, and stroke increased with severity of overweight among both women and men. Compared with their same-sex peers with a body mass index (BMI) (calculated as weight in kilograms divided by the square of height in meters) between 18.5 and 24.9, those with BMI of 35.0 or more were approximately 20 times more likely to develop diabetes (relative risk [RR], 17.0; 95% confidence interval [CI], 14.2-20.5 for women; RR, 23.4; 95% CI, 19.4-33.2 for men). Women who were overweight but not obese (ie, BMI between 25.0 and 29.9) were also significantly more likely than their leaner peers to develop gallstones (RR, 1.9), hypertension (RR, 1.7), high cholesterol level (RR, 1.1), and heart disease (RR, 1.4). The results were similar in men. CONCLUSIONS: During 10 years of follow-up, the incidence of diabetes, gallstones, hypertension, heart disease, colon cancer, and stroke (men only) increased with degree of overweight in both men and women. Adults who were overweight but not obese (ie, 25.0 < or = BMI < or = 29.9) were at significantly increased risk of developing numerous health conditions. Moreover, the dose-response relationship between BMI and the risk of developing chronic diseases was evident even among adults in the upper half of the healthy weight range (ie, BMI of 22.0-24.9), suggesting that adults should try to maintain a BMI between 18.5 and 21.9 to minimize their risk of disease.24:T8f5,OBJECTIVE: To evaluate the impact of a school-based health behavior intervention known as Planet Health on obesity among boys and girls in grades 6 to 8. DESIGN: Randomized, controlled field trial with 5 intervention and 5 control schools. Outcomes were assessed using preintervention (fall 1995) and follow-up measures (spring 1997), including prevalence, incidence, and remission of obesity. PARTICIPANTS: A group of 1295 ethnically diverse grade 6 and 7 students from public schools in 4 Massachusetts communities. INTERVENTION: Students participated in a school-based interdisciplinary intervention over 2 school years. Planet Health sessions were included within existing curricula using classroom teachers in 4 major subjects and physical education. Sessions focused on decreasing television viewing, decreasing consumption of high-fat foods, increasing fruit and vegetable intake, and increasing moderate and vigorous physical activity. MAIN OUTCOME MEASURES: Obesity was defined as a composite indicator based on both a body mass index and a triceps skinfold value greater than or equal to age- and sex-specific 85th percentiles. Because schools were randomized, rather than students, the generalized estimating equation method was used to adjust for individual-level covariates under cluster randomization. RESULTS: The prevalence of obesity among girls in intervention schools was reduced compared with controls, controlling for baseline obesity (odds ratio, 0.47; 95% confidence interval, 0.24-0.93; P = .03), with no differences found among boys. There was greater remission of obesity among intervention girls vs. control girls (odds ratio, 2.16; 95% confidence interval, 1.07-4.35; P = .04). The intervention reduced television hours among both girls and boys, and increased fruit and vegetable consumption and resulted in a smaller increment in total energy intake among girls. Reductions in television viewing predicted obesity change and mediated the intervention effect. Among girls, each hour of reduction in television viewing predicted reduced obesity prevalence (odds ratio, 0.85; 95% confidence interval, 0.75-0.97; P = .02). CONCLUSION: Planet Health decreased obesity among female students, indicating a promising school-based approach to reducing obesity among youth.25:T448,The general linear mixed model provides a useful approach for analysing a wide variety of data structures which practising statisticians often encounter. Two such data structures which can be problematic to analyse are unbalanced repeated measures data and longitudinal data. Owing to recent advances in methods and software, the mixed model analysis is now readily available to data analysts. The model is similar in many respects to ordinary multiple regression, but because it allows correlation between the observations, it requires additional work to specify models and to assess goodness-of-fit. The extra complexity involved is compensated for by the additional flexibility it provides in model fitting. The purpose of this tutorial is to provide readers with a sufficient introduction to the theory to understand the method and a more extensive discussion of model fitting and checking in order to provide guidelines for its use. We provide two detailed case studies, one a clinical trial with repeated measures and dropouts, and one an epidemiological survey with longitudinal follow-up.26:T4e0,This paper presents a general mixed model for the analysis of serial dichotomous responses provided by a panel of study participants. Each subject's serial responses are assumed to arise from a logistic model, but with regression coefficients that vary between subjects. The logistic regression parameters are assumed to be normally distributed in the population. Inference is based upon maximum likelihood estimation of fixed effects and variance components, and empirical Bayes estimation of random effects. Exact solutions are analytically and computationally infeasible, but an approximation based on the mode of the posterior distribution of the random parameters is proposed, and is implemented by means of the EM algorithm. This approximate method is compared with a simpler two-step method proposed by Korn and Whittemore (1979, Biometrics 35, 795-804), using data from a panel study of asthmatics originally described in that paper. One advantage of the estimation strategy described here is the ability to use all of the data, including that from subjects with insufficient data to permit fitting of a separate logistic regression model, as required by the Korn and Whittemore method. However, the new method is computationally intensive.27:T619,BACKGROUND AND METHODS: By matching the medical records of a random sample of 31,429 patients hospitalized in New York State in 1984 with statewide data on medical-malpractice claims, we identified patients who had filed claims against physicians and hospitals. These results were then compared with our findings, based on a review of the same medical records, regarding the incidence of injuries to patients caused by medical management (adverse events). RESULTS: We identified 47 malpractice claims among 30,195 patients' records located on our initial visits to the hospitals, and 4 claims among 580 additional records located during follow-up visits. The overall rate of claims per discharge (weighted) was 0.13 percent (95 percent confidence interval, 0.076 to 0.18 percent). Of the 280 patients who had adverse events caused by medical negligence as defined by the study protocol, 8 filed malpractice claims (weighted rate, 1.53 percent; 95 percent confidence interval, 0 to 3.2 percent). By contrast, our estimate of the statewide ratio of adverse events caused by negligence (27,179) to malpractice claims (3570) is 7.6 to 1. This relative frequency overstates the chances that a negligent adverse event will produce a claim, however, because most of the events for which claims were made in the sample did not meet our definition of adverse events due to negligence. CONCLUSIONS: Medical-malpractice litigation infrequently compensates patients injured by medical negligence and rarely identifies, and holds providers accountable for, substandard care.28:T418,This report summarizes morbidity in 151 patients in a cooperative trial designed to evaluate the clinical effects of different dialysis prescriptions. Four treatment groups were divided along two dimensions: dialysis treatment time (long or short), and blood urea nitrogen (BUN) concentration averaged with respect to time (TACurea) (high or low). Dietary protein was not restricted. There was no difference in mortality between the groups. Withdrawal of patients from the high-BUN groups for medical reasons was significantly greater than withdrawal from the low-BUN groups. Hospitalization was also greater in the high-BUN groups, but dialysis treatment time had no significant effects. The data indicate that the occurrence of morbid events is affected by the dialysis prescription. Increased morbidity appears to accompany prescriptions associated with a relatively high BUN. Conversely, morbidity may be decreased by prescriptions associated with more efficient removal of urea if the dietary intake of protein and other nutrients is adequate.29:T4ac,In the study of complex traits, the utility of linkage analysis and single marker association tests can be limited for researchers attempting to elucidate the complex interplay between a gene and environmental covariates. For these purposes, tests of gene-environment interactions are needed. In addition, recent studies have indicated that haplotypes, which are specific combinations of nucleotides on the same chromosome, may be more suitable as the unit of analysis for statistical tests than single genetic markers. The difficulty with this approach is that, in standard laboratory genotyping, haplotypes are often not directly observable. Instead, unphased marker phenotypes are collected. In this article, we present a method for estimating and testing haplotype-environment interactions when linkage phase is potentially ambiguous. The method builds on the work of Schaid et al. [2002] and is applicable to any trait that can be placed in the generalized linear model framework. Simulations were run to illustrate the salient features of the method. In addition, the method was used to test for haplotype-smoking exposure interaction with data from the Childhood Asthma Management Program.2a:T467,We compare two statistical methods for combining event rates from several studies. Both methods treat each study as a separate stratum. The Peto-modified Mantel-Haenszel (Peto) method estimates a combined odds ratio assuming homogeneity across strata and provides a test for heterogeneity. The DerSimonian and Laird modified Cochran method (D&L) produces a weighted average of rate differences, where the weights allow for among-study variability. We analyse 22 meta-analyses from ten reports by both methods. The pooled estimates are divided by their standard errors to produce a Z-statistic. A t-test comparing Z-statistics from all 22 studies suggests that the D&L method tends to be more conservative [d(Peto - D&L) = 0.29, t = 2.53, p = 0.02]. For a subset of 14 non-heterogeneous studies, the difference is smaller and non-significant (d = 0.09, t = 0.72, p = 0.49). The results from the methods correlate well (r = 0.66 for all 22 studies, r = 0.95 for 14 non-heterogeneous studies). Thus, the presence of heterogeneity influences our conclusion. We discuss the statistical and scientific implications of these findings.2b:T13e2,The purpose of this article is to consider the use of the EM algorithm (Dempster, Laird, and Rubin 1977) for both maximum likelihood (ML) and restricted maximum likelihood (REML) estimation in a general repeated measures setting using a multivariate normal data model with linear mean and covariance structure (Anderson 1973). Several models and methods of analysis have been proposed in recent years for repeated measures data; Ware (1985) presented an overview. Because the EM algorithm is a general-purpose, iterative method for computing ML estimates with incomplete data, it has often been used in this particular setting (Dempster et al. 1977; Andrade and Helms 1984; Jennrich and Schluchter 1985). There are two apparently different approaches to using the EM algorithm in this setting. In one application, each experimental unit is observed under a standard protocol specifying measurements at each of n occasions (or under n conditions), and incompleteness implies that the number of measurements actually collected on each unit is less than the requisite n for at least some units. In this circumstance, incompleteness may be modeled if one regards the measurements actually collected as the observed data, the conceptual set of n measurements on each individual as the complete data, and the unobserved data as the missing measurements on those units with fewer than n observations. Application of the EM algorithm in this setting [referred to as “missing data” in Dempster et al. (1977) and “incomplete data” in Jennrich and Schluchter (1985)] was discussed by Orchard and Woodbury (1972), Beale and Little (1975), and Jennrich and Schluchter (1985). One drawback of this approach in the longitudinal data setting is that the multivariate model with linear mean and covariance structure does not, in general, possess closed-form solutions even with complete data (Anderson 1973; Szatrowski 1980). Thus implementing the EM algorithm requires either an iterative M step within each EM iteration or the use of a generalized EM (GEM) algorithm that requires only that the complete data likelihood be increased rather than maximized at each M step. A second drawback is that this approach requires specification of the covariates for both the observed and the missing observations. If the covariates are unknown for the missing observations, arbitrary values must be specified, which may affect the rate but not the final point of convergence (Jennrich and Schluchter 1985). The second application of the EM algorithm arises naturally when we use mixed models to analyze serial measurements. In this setting, the incomplete data are modeled quite differently. The observed data are as before, that is, the measurements actually collected on each unit. The complete data, however, consist of the observed data plus the unobservable random parameters and error terms specified in the mixed model. Thus the missing data (the random parameters and error terms) would not be viewed as data in the traditional statistical sense. Laird and Ware (1982) and Andrade and Helms (1984) took this approach. This article shows that the latter approach is more general and encompasses the missing-data approach as a special case. This result has several important applications. First, it means that EM algorithms encoded for models with random effects can also be used for multivariate normal models with arbitrary covariance structure and missing data. Second, this approach avoids specification of covariates for missing observations. Finally, use of the general formulation means that closed-form solutions for the complete data maximization will exist for a much broader class of models, enabling one to avoid use of GEM or iterations within each M step. For a certain class of multivariate growth curve models with random effects structure (Reinsel 1982), closed-form solutions exist for both ML and REML estimates of the mean and covariance parameters. Formulas for these closed-form solutions are given that are applicable whenever the solution is not on the boundary. The choice of starting values for the EM iterations is important, since the EM algorithm will not, in general, converge from arbitrary starting values to the closed-form solution (if it exists) in one iteration. Several possibilities for starting values are given. The rate of convergence of the EM algorithm is generally linear. The actual speed of convergence in two data examples is shown to depend heavily on both the actual data set and the assumed structure for the covariance matrix. We discuss two methods for accelerating convergence, which we find are most useful when the covariance matrix is assumed to have a random effects structure. When the covariance matrix is assumed to be arbitrary, the EM iterations reduce to familiar iteratively reweighted least squares (IRLS) computations. The EM algorithm has the unusual property in this setting that when all of the data are complete (no missing observations), the iterations are still IRLS, but the rate of convergence changes from linear to quadratic.2c:T6cc,Attention deficit hyperactivity disorder (ADHD) is a complex condition with environmental and genetic etiologies. Up to this point, research has identified genetic associations with candidate genes from known biological pathways. In order to identify novel ADHD susceptibility genes, 600,000 SNPs were genotyped in 958 ADHD proband-parent trios. After applying data cleaning procedures we examined 429,981 autosomal SNPs in 909 family trios. We generated six quantitative phenotypes from 18 ADHD symptoms to be used in genome-wide association analyses. With the PBAT screening algorithm, we identified 2 SNPs, rs6565113 and rs552655 that met the criteria for significance within a specified phenotype. These SNPs are located in intronic regions of genes CDH13 and GFOD1, respectively. CDH13 has been implicated previously in substance use disorders. We also evaluated the association of SNPs from a list of 37 ADHD candidate genes that was specified a priori. These findings, along with association P-values with a magnitude less than 10(-5), are discussed in this manuscript. Seventeen of these candidate genes had association P-values lower then 0.01: SLC6A1, SLC9A9, HES1, ADRB2, HTR1E, DDC, ADRA1A, DBH, DRD2, BDNF, TPH2, HTR2A, SLC6A2, PER1, CHRNA4, SNAP25, and COMT. Among the candidate genes, SLC9A9 had the strongest overall associations with 58 association test P-values lower than 0.01 and multiple association P-values at a magnitude of 10(-5) in this gene. In sum, these findings identify novel genetic associations at viable ADHD candidate genes and provide confirmatory evidence for associations at previous candidate genes. Replication of these results is necessary in order to confirm the proposed genetic variants for ADHD.2d:T54e,We provide a general purpose family-based testing strategy for associating disease phenotypes with haplotypes when phase may be ambiguous and parental genotype data may be missing. These tests for linkage and association can be used in candidate gene studies with tightly linked markers. Our proposed weighted conditional approach extends the method described in Rabinowitz and Laird to multiple markers. It is attractive because it provides haplotype tests for family-based studies that are efficient and robust to population admixture, phenotype distribution specification, and ascertainment based on phenotypes. It can handle missing parental genotypes and/or missing phase in both offspring and parents. It yields either haplotype-specific (univariate) tests or multi-haplotype (global) tests. This extension has been implemented in the freely available software haplotype FBAT. We used the haplotype FBAT program to test for associations between asthma phenotypes and single nucleotide polymorphisms (SNPs) in the beta-2 adrenergic receptor gene. Whereas no single SNP showed significant association with asthma diagnosis or bronchodilator responsiveness (quantitative trait), a haplotype-based global test found a highly significant association with asthma diagnosis (P value <0.00005) and the measure of bronchodilator responsiveness (P value =0.016).2e:Ta32,OBJECTIVE: To evaluate the impact of a school-based interdisciplinary health behavior intervention on diet and physical activity among children in grades 4 and 5. DESIGN: A quasiexperimental field trial with 6 intervention and 8 matched control schools. Outcomes were assessed longitudinally using preintervention (fall 1995) and follow-up (spring 1997) student survey food frequency and activity measures and follow-up 24-hour recall measures of diet and activity. Change was also assessed using yearly repeated cross-sectional surveys of all grade 5 students from 1995 through 1997. PARTICIPANTS: Longitudinal data were collected from 479 students initially in grade 4 in Baltimore, Md, public schools; 91% were African American. Repeated 24-hour recall measures in 1997 were collected for a random subsample of 336 students. Cross-sectional survey data were collected from all grade 5 students in 1995,1996, and 1997 (n = 2103). INTERVENTION: The Eat Well and Keep Moving Program was taught by classroom teachers over 2 years in math, science, language arts, and social studies classes. Materials provided links to school food services and families and provided training and wellness programs for teachers and other staff members. Intervention materials focused on decreasing consumption of foods high in total and saturated fat and increasing fruit and vegetable intake, as well as reducing television viewing and increasing physical activity. MAIN OUTCOME MEASURES: Dietary intake and physical activity measured via repeated 24-hour recall were primary end points, with additional food frequency and activity measures. RESULTS: The 24-hour recall measures indicated that, after controlling for baseline covariates, the percentages of total energy from fat and saturated fat were reduced among students in intervention compared with control schools (-1.4%; 95% confidence interval [CI], -2.8 to -0.04; P = .04 and -0.60%; 95% CI, -1.2 to -0.01; P = .05). There was an increase in fruit and vegetable intake (0.36 servings/4184 kJ; 95% CI, 0.10-0.62; P=.01), in vitamin C intake (8.8 mg/4184 kJ; 95% CI, 2.0-16; P=.01), and in fiber consumption (0.7 g/4184 kJ; 95% CI, 0.0-1.4; P=.05). Television viewing was marginally reduced (-0.55 h/d; 95% CI, -1.04 to 0.04; P=.06). Analysis of longitudinal and repeated cross-sectional food frequency data indicated similar significant decreases in the percentages of total energy from fat and saturated fat. CONCLUSION: Evaluation of the Eat Well and Keep Moving Program indicates effectiveness in improving dietary intake of students and reducing television viewing.2f:T5c6,BACKGROUND: Common treatments used for severe psoriasis include psoralen and ultraviolet A radiation (PUVA), methotrexate, ultraviolet B (UVB), and tar. These therapies are often used for prolonged periods and may be carcinogenic. METHODS: For more than 13 years, the authors have prospectively determined the incidence of skin cancer and use of treatments for psoriasis in a 1380 patient cohort originally enrolled in a therapeutic trial of PUVA at 16 university centers. RESULTS: Squamous cell carcinoma (SCC) developed in more than one fourth of patients exposed to high doses of PUVA. In this group, the standard morbidity ratio for these tumors was 83 (95% confidence interval [CI], 72-96) compared with the expected number of these tumors in the general population. High-level exposure to methotrexate is a significant independent risk factor for developing SCC (relative risk, 2.1 for high versus low or no exposure; 95% CI, 1.4-2.8). Metastatic disease developed in seven patients with SCC. No significant increase in the risk of SCC was associated with long term exposure to UVB or topical tar, and no substantial increase in the risk of basal cell carcinoma was noted in association with prolonged use of any of these treatments. CONCLUSIONS: Long term exposure to PUVA and methotrexate significantly increases the risk of SCC in patients with psoriasis. This risk should be considered in selection of treatment. The ultimate morbidity of these tumors is undetermined.30:T6aa,BACKGROUND: According to epidemiologic studies that use recall of lifetime episodes, the prevalence of depression is increasing. This report from the Stirling County Study compares rates of current depression among representative samples of adults from a population in Atlantic Canada. METHODS: Sample sizes were 1003, 1201, and 1396 in 1952, 1970, and 1992, respectively. The depression component of the study's method, the DPAX (DP for depression and AX for anxiety), was employed. The original procedure (DPAX-1) was applied in all years. A revision (DPAX-2) was used in 1970 and 1992. The Diagnostic Interview Schedule (DIS) was also used in 1992. RESULTS: With the DPAX-1, the overall prevalence of current depression was steady at 5% over the 2 early samples but declined in 1992 because of vernacular changes referring to dysphoria. The DPAX-2 gave a stable overall prevalence of 5% in the 2 recent samples, but indicated that women and younger people were at greater risk in 1992 than in 1970. The DIS, like the DPAX-2, found a current 1992 rate of 5% for major depressive episodes combined with dysthymia. Recalled lifetime rates using the DIS showed the same profile interpreted in other studies as suggesting an increase in depression over time. CONCLUSIONS: Three samples over a 40-year period showed a stable current prevalence of depression using the DPAX methods that was comparable in 1992 with the current rates using the DIS. This casts doubt on the interpretation that depression is generally increasing. Within the overall steady rate observed in this study, historical change was a matter of redistribution by sex and age, with a higher rate among younger women being of recent origin.31:T7ba,BACKGROUND: Prior studies suggest that certain psychiatric and medical disorders co-occur with binge eating disorder (BED). However, there has been no large, community-based study with diagnoses made by clinician interviewers. We used data from that type of study to assess the co-occurrence of various psychiatric and medical disorders with DSM-IV BED and with subthreshold BED. METHOD: From October 2002 to July 2004, we interviewed 150 probands with BED, 150 probands without BED, and 888 of their first-degree relatives (135 of whom had BED, and 54 of whom met specific partial criteria for BED that we defined as subthreshold BED). Study participants were interviewed using the Structured Clinical Interview for DSM-IV to assess BED and other psychiatric disorders and a supplemental structured interview to assess certain medical disorders; participants also completed a self-report questionnaire, the Bad Things Scale. For each psychiatric and medical disorder, we calculated the age- and sex-adjusted co-occurrence odds ratio: the odds of having that disorder in one's lifetime among individuals with (full or subthreshold) lifetime BED compared to individuals without lifetime BED. We also used subjects' responses to the Bad Things Scale to adjust for adversity over-reporting, a type of response bias that could result in spurious findings of co-occurrence. RESULTS: Full BED co-occurred significantly with bipolar disorder, major depressive disorder, bulimia nervosa but not anorexia nervosa, most anxiety disorders, substance use disorders, body dysmorphic disorder, kleptomania, irritable bowel syndrome, and fibromyalgia. These results changed little after correcting for adversity over-reporting. Subthreshold BED co-occurred significantly with many, but not all, of the significantly co-occurring disorders for full BED. CONCLUSION: BED and, to a lesser degree, subthreshold BED exhibit substantial lifetime co-occurrence with psychiatric and medical disorders.32:T52a,In this paper, we review analytic methods for regression models for longitudinal categorical responses. We focus on both likelihood-based approaches and non-likelihood approaches to analysing repeated binary responses. In both approaches, interest is focussed primarily on the regression parameters for the marginal expectations of the binary responses. The association or time dependence between the responses is largely regarded as a nuisance characteristic of the data. We consider these approaches for both the complete and incomplete data cases. We describe the generalized estimating equations (GEE) approach, a non-likelihood approach, and some proposed extensions of it. We also discuss likelihood-based approaches that are based on a log-linear representation of the joint probabilities of the binary responses. We describe how a likelihood-based "mixed parameter" model yields likelihood equations for the regression parameters that are of exactly the same form as the GEE. An outline of the desirable features and drawbacks of each approach is presented. In addition, we provide some comparisons in terms of asymptotic relative efficiency for the complete data case, and in terms of asymptotic bias for the incomplete data case. Finally, we make some recommendations concerning the application of these methods.16:["$","div",null,{"className":"mx-auto max-w-6xl px-4 py-6 sm:px-6 sm:py-10","children":[["$","header",null,{"className":"grid gap-6 lg:grid-cols-[1fr_22rem] lg:items-end","children":[["$","div",null,{"children":[["$","p",null,{"className":"label mb-2 text-accent","children":["Researcher profile · ","OpenAlex"]}],["$","h1",null,{"className":"text-3xl font-bold sm:text-4xl","children":"Nan M. 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The condition was defined as not less than a 30-dB loss over three contiguous frequencies in three days or less. Follow-up audiograms were obtained four weeks and three months later. Specific audiologic guidelines for the assessment of hearing recovery were used to ensure objectivity. Steroids had a statistically significant effect on the recovery of hearing in patients with moderate hearing losses. The nature of the hearing loss and its susceptibility to improvement with steroid therapy lend support to the hypothesis that viral cochlitis is the primary cause of ISHL.","authors":[{"name":"Wesley R. Wilson","id":"openalex:A5009611644"},{"name":"Frederick M. Byl","id":"openalex:A5050604535"},{"name":"Nan M. 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The test statistic is basically the covariance between a user-specified function of the genotype and a user-specified function of the trait. The distribution of the statistic is computed using the appropriate conditional distribution of offspring genotypes that adjusts for admixture.","authors":[{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA. laird@biostat.harvard.edu","‖Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts"],"corresponding":true},{"name":"Steve Horvath","id":"openalex:A5000192581","orcid":"https://orcid.org/0000-0002-4110-3589","affiliation":"University of Bonn","institutionId":"I135140700","institutionUrl":"https://ror.org/041nas322","affiliations":["Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts","Institute for Medical Biometry, Informatics, and Epidemiology, University of Bonn, Bonn, Germany"]},{"name":"Xin Ping Xu","id":"openalex:A5037645443","orcid":"https://orcid.org/0000-0002-6324-5211","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Program in Population Genetics, Harvard School of Public Health, Boston, Massachusetts"]}],"venue":"Genetic Epidemiology","venueId":"S136352176","publisher":"Wiley","publisherId":"P4310320595","year":2000,"publishedDate":"2000-01-01","type":"article","citationCount":856,"referenceCount":15,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1002/1098-2272(2000)19:1+<::aid-gepi6>3.0.co;2-m","topics":[{"id":"T10261","name":"Genetic Associations and Epidemiology","score":0.986299991607666,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T11468","name":"Genetic Mapping and Diversity in Plants and Animals","score":0.006899999920278788,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T10751","name":"Forensic and Genetic Research","score":0.002099999925121665,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"}],"sources":["openalex"],"id":"openalex:W1977945634"},{"ids":{"openalex":"W1997817781","doi":"10.1080/01621459.1978.10480103"},"title":"Nonparametric Maximum Likelihood Estimation of a Mixing Distribution","abstract":"The nonparametric maximum likelihood estimate of a mixing distribution is shown to be self-consistent, a property which characterizes the nonparametric maximum likelihood estimate of a distribution function in incomplete data problems. Under various conditions the estimate is a step function, with a finite number of steps. Its computation is illustrated with a small example.","authors":[{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Harvard University","Department of Statistics, Department of Biostatistics , Harvard University , Cambridge , MA , 02138 , USA"],"corresponding":true}],"venue":"Journal of the American Statistical Association","venueId":"S4394736638","publisher":null,"year":1978,"publishedDate":"1978-12-01","type":"article","citationCount":753,"referenceCount":33,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1080/01621459.1978.10480103","topics":[{"id":"T11901","name":"Bayesian Methods and Mixture Models","score":0.9984999895095825,"subfieldId":"1702","subfield":"Artificial Intelligence","fieldId":"17","field":"Computer Science"},{"id":"T10968","name":"Statistical Distribution Estimation and Applications","score":0.000699999975040555,"subfieldId":"2613","subfield":"Statistics and Probability","fieldId":"26","field":"Mathematics"},{"id":"T13500","name":"Probability and Statistical Research","score":0.00019999999494757503,"subfieldId":"2613","subfield":"Statistics and Probability","fieldId":"26","field":"Mathematics"}],"sources":["openalex"],"id":"openalex:W1997817781"},{"ids":{"openalex":"W2315295323","doi":"10.1038/sj.ejhg.5200625","pmid":"11313775"},"title":"The family based association test method: strategies for studying general genotype–phenotype associations","authors":[{"name":"Steve Horvath","id":"openalex:A5000192581","orcid":"https://orcid.org/0000-0002-4110-3589","affiliation":"University of Bonn","institutionId":"I135140700","institutionUrl":"https://ror.org/041nas322","affiliations":["Institute for Medical Statistics & Genetic Epidemiology, University of Bonn, Bonn, Germany. shorvath@mednet.ucla.edu","Department of Biostatistics, University of California at Los Angeles, Los Angeles, USA","Department of Human Genetics, University of California at Los Angeles, Los Angeles, USA","Institute for Medical Statistics & Genetic Epidemiology, University of Bonn, Bonn, Germany"],"corresponding":true},{"name":"Xin Ping Xu","id":"openalex:A5037645443","orcid":"https://orcid.org/0000-0002-6324-5211","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Program for Population Genetics, Harvard School of Public Health, Boston, Masachusetts, USA","Program for Population Genetics, Harvard School of Public Health, Boston, USA"]},{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA","Department of Biostatistics, Harvard School of Public Health, Boston, USA"]}],"venue":"European Journal of Human Genetics","venueId":"S121703463","publisher":"Springer Nature","publisherId":"P4310319965","year":2001,"publishedDate":"2001-04-01","type":"article","citationCount":739,"referenceCount":11,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1038/sj.ejhg.5200625","topics":[{"id":"T10261","name":"Genetic Associations and Epidemiology","score":0.9955999851226807,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T11468","name":"Genetic Mapping and Diversity in Plants and Animals","score":0.0027000000700354576,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T10012","name":"Genetic diversity and population structure","score":0.00039999998989515007,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"}],"sources":["openalex"],"id":"openalex:W2315295323"},{"ids":{"openalex":"W2149512324","doi":"10.1126/science.1124779","pmid":"16614226"},"title":"A Common Genetic Variant Is Associated with Adult and Childhood Obesity","abstract":"Obesity is a heritable trait and a risk factor for many common diseases such as type 2 diabetes, heart disease, and hypertension. We used a dense whole-genome scan of DNA samples from the Framingham Heart Study participants to identify a common genetic variant near the INSIG2 gene associated with obesity. We have replicated the finding in four separate samples composed of individuals of Western European ancestry, African Americans, and children. The obesity-predisposing genotype is present in 10% of individuals. Our study suggests that common genetic polymorphisms are important determinants of obesity.","authors":[{"name":"Alan G. Herbert","id":"openalex:A5029296269","orcid":"https://orcid.org/0000-0002-0093-1572","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"],"corresponding":true},{"name":"Norman P. Gerry","id":"openalex:A5002408945","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Matthew B. McQueen","id":"openalex:A5073380718","orcid":"https://orcid.org/0000-0002-7035-6112","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Iris M. Heid","id":"openalex:A5074235125","orcid":"https://orcid.org/0000-0002-4122-5308","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Arne Pfeufer","id":"openalex:A5015828408","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Thomas Illig","id":"openalex:A5110353291","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"H.‐Erich Wichmann","id":"openalex:A5108482146","orcid":"https://orcid.org/0000-0002-1721-3796","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Thomas Meitinger","id":"openalex:A5006468847","orcid":"https://orcid.org/0000-0002-8838-8403","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"David Hunter","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Frank B. Hu","id":"openalex:A5056459536","orcid":"https://orcid.org/0000-0002-8233-6274","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Graham A. Colditz","id":"openalex:A5031201037","orcid":"https://orcid.org/0000-0002-7307-0291","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Anke Hinney","id":"openalex:A5003455118","orcid":"https://orcid.org/0000-0001-5659-0706","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Johannes Hebebrand","id":"openalex:A5038515289","orcid":"https://orcid.org/0000-0001-5364-6073","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Kerstin Koberwitz","id":"openalex:A5060556754","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Xiaofeng Zhu","id":"openalex:A5007766399","orcid":"https://orcid.org/0000-0003-0037-411X","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Richard Cooper","id":"openalex:A5063376287","orcid":"https://orcid.org/0000-0002-3037-7779","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Kristin Ardlie","id":"openalex:A5057715705","orcid":"https://orcid.org/0000-0003-4272-6283","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Helen N. Lyon","id":"openalex:A5067979974","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Joel N. Hirschhorn","id":"openalex:A5001552586","orcid":"https://orcid.org/0000-0002-1650-7901","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"]},{"name":"Marc E. 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Christman","id":"openalex:A5108528063","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA","Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA","Institute of Epidemiology, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","Institute of Human Genetics, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany","KORA Group, Forschungszentrum für Umwelt und Gesundheit, D-85764 Neuherberg, Germany"],"corresponding":true}],"venue":"Science","venueId":"S3880285","publisher":"American Association for the Advancement of Science","publisherId":"P4310315823","year":2006,"publishedDate":"2006-04-13","type":"article","citationCount":734,"referenceCount":32,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1126/science.1124779","topics":[{"id":"T10261","name":"Genetic Associations and Epidemiology","score":0.989300012588501,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T13354","name":"Genetics and Physical Performance","score":0.0032999999821186066,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T13984","name":"Nutrition, Genetics, and Disease","score":0.0017999999690800905,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"}],"sources":["openalex"],"id":"openalex:W2149512324"},{"ids":{"openalex":"W2024085858","doi":"10.2307/2531147","pmid":"6534418"},"title":"Random-Effects Models for Serial Observations with Binary Response","abstract":"$26","authors":[{"name":"Robert G. Stiratelli","id":"openalex:A5090376501"},{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234"},{"name":"James H. Ware","id":"openalex:A5104064089"}],"venue":"Biometrics","venueId":"S8265502","publisher":"Oxford University Press","publisherId":"P4310311648","year":1984,"publishedDate":"1984-12-01","type":"article","citationCount":734,"referenceCount":17,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.2307/2531147","topics":[{"id":"T10243","name":"Statistical Methods and Bayesian Inference","score":0.9990000128746033,"subfieldId":"2613","subfield":"Statistics and Probability","fieldId":"26","field":"Mathematics"},{"id":"T11911","name":"Spatial and Panel Data Analysis","score":0.00019999999494757503,"subfieldId":"2002","subfield":"Economics and Econometrics","fieldId":"20","field":"Economics, Econometrics and Finance"},{"id":"T11235","name":"Statistical Methods in Clinical Trials","score":0.00009999999747378752,"subfieldId":"2613","subfield":"Statistics and Probability","fieldId":"26","field":"Mathematics"}],"sources":["openalex"],"id":"openalex:W2024085858"},{"ids":{"openalex":"W2322377061","doi":"10.1056/nejm199107253250405","pmid":"2057025"},"title":"Relation between Malpractice Claims and Adverse Events Due to Negligence","abstract":"$27","authors":[{"name":"A. Russell Localio","id":"openalex:A5037564379","orcid":"https://orcid.org/0000-0002-0506-851X","affiliation":"Pennsylvania State University","institutionId":"I130769515","institutionUrl":"https://ror.org/04p491231","affiliations":["Center for Biostatistics and Epidemiology, Penn State University College of Medicine, Hershey 17033","Center for Biostatistics and Epidemiology, Penn State University College of Medicine, Hershey, Harvard School of Public Health, Boston","Departments of Health Policy and Management, Harvard School of Public Health, Boston"]},{"name":"Ann G. Lawthers","id":"openalex:A5055685649","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Departments of Health Policy and Management, Harvard School of Public Health, Boston"]},{"name":"Troyen A. Brennan","id":"openalex:A5103949633","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston"]},{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Biostatistics, Harvard School of Public Health, Boston"]},{"name":"Liesi E. Hebert","id":"openalex:A5079821988","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Departments of Health Policy and Management, Harvard School of Public Health, Boston"]},{"name":"Lynn M. Peterson","id":"openalex:A5067798355","affiliation":"Division of Medical Ethics, Department of Social Medicine","affiliations":["Division of Medical Ethics, Department of Social Medicine"]},{"name":"Joseph P. Newhouse","id":"openalex:A5055066308","orcid":"https://orcid.org/0000-0001-5837-3203","affiliation":"Pennsylvania State University","institutionId":"I130769515","institutionUrl":"https://ror.org/04p491231","affiliations":["Department of Health Care Policy, Harvard Medical School, Boston","Departments of Health Policy and Management, Harvard School of Public Health, Boston","Kennedy School of Government, Harvard University, Cambridge, Mass., . Address reprint requests to Mr. Localio at the Center for Biostatistics and Epidemiology, Penn State University College of Medicine, P.O. Box 850, Hershey, PA 17033"]},{"name":"Paul C. Weiler","id":"openalex:A5082744061","affiliation":"Pennsylvania State University","institutionId":"I130769515","institutionUrl":"https://ror.org/04p491231","affiliations":["Harvard Law School, Cambridge, Mass., . Address reprint requests to Mr. Localio at the Center for Biostatistics and Epidemiology, Penn State University College of Medicine, P.O. Box 850, Hershey, PA 17033"]},{"name":"Howard H. Hiatt","id":"openalex:A5111631967","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston","Departments of Health Policy and Management, Harvard School of Public Health, Boston"]}],"venue":"New England Journal of Medicine","venueId":"S62468778","publisher":"Massachusetts Medical Society","publisherId":"P4310320239","year":1991,"publishedDate":"1991-07-25","type":"article","citationCount":719,"referenceCount":15,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.1056/nejm199107253250405","oaUrl":"https://www.nejm.org/doi/pdf/10.1056/NEJM199107253250405?articleTools=true","pdfUrl":"https://www.nejm.org/doi/pdf/10.1056/NEJM199107253250405?articleTools=true","topics":[{"id":"T12339","name":"Medical Malpractice and Liability Issues","score":0.9994999766349792,"subfieldId":"3611","subfield":"Pharmacy","fieldId":"36","field":"Health Professions"},{"id":"T10508","name":"Patient Safety and Medication Errors","score":0.0003000000142492354,"subfieldId":"3604","subfield":"Emergency Medical Services","fieldId":"36","field":"Health Professions"},{"id":"T12871","name":"Healthcare Quality and Management","score":0,"subfieldId":"3605","subfield":"Health Information Management","fieldId":"36","field":"Health Professions"}],"sources":["openalex"],"id":"openalex:W2322377061"},{"ids":{"openalex":"W2161760321","doi":"10.1002/sim.4780070131","pmid":"3353609"},"title":"Missing data in longitudinal studies","abstract":"When observations are made repeatedly over time on the same experimental units, unbalanced patterns of observations are a common occurrence. This complication makes standard analyses more difficult or inappropriate to implement, means loss of efficiency, and may introduce bias into the results as well. Some possible approaches to dealing with missing data include complete case analyses, univariate analyses with adjustments for variance estimates, two-step analyses, and likelihood based approaches. Likelihood approaches can be further categorized as to whether or not an explicit model is introduced for the non-response mechanism. This paper will review the use of likelihood based analyses for longitudinal data with missing responses, both from the point of view of ease of implementation and appropriateness in view of the non-response mechanism. Models for both measured and dichotomous outcome data will be discussed. The appropriateness of some non-likelihood based analyses is briefly considered.","authors":[{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115","Department of Biostatïstics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, U.S.A"],"corresponding":true}],"venue":"Statistics in Medicine","venueId":"S55189604","publisher":"Wiley","publisherId":"P4310320595","year":1988,"publishedDate":"1988-01-01","type":"article","citationCount":713,"referenceCount":41,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1002/sim.4780070131","topics":[{"id":"T10243","name":"Statistical Methods and Bayesian Inference","score":1,"subfieldId":"2613","subfield":"Statistics and Probability","fieldId":"26","field":"Mathematics"},{"id":"T11235","name":"Statistical Methods in Clinical Trials","score":0,"subfieldId":"2613","subfield":"Statistics and Probability","fieldId":"26","field":"Mathematics"},{"id":"T13487","name":"Statistical and numerical algorithms","score":0,"subfieldId":"2604","subfield":"Applied Mathematics","fieldId":"26","field":"Mathematics"}],"sources":["openalex"],"id":"openalex:W2161760321"},{"ids":{"openalex":"W2009730157","doi":"10.1056/nejm198111123052003","pmid":"7027040"},"title":"Effect of the Hemodialysis Prescription on Patient Morbidity","abstract":"$28","authors":[{"name":"Edmund G. Lowrie","id":"openalex:A5111639590"},{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234"},{"name":"THOMAS F. PARKER","id":"openalex:A5049667081","orcid":"https://orcid.org/0000-0001-8043-0243"},{"name":"JOHN A. SARGENT","id":"openalex:A5050894454","orcid":"https://orcid.org/0000-0001-8293-8569"}],"venue":"New England Journal of Medicine","venueId":"S62468778","publisher":"Massachusetts Medical Society","publisherId":"P4310320239","year":1981,"publishedDate":"1981-11-12","type":"article","citationCount":708,"referenceCount":10,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1056/nejm198111123052003","topics":[{"id":"T10291","name":"Dialysis and Renal Disease Management","score":1,"subfieldId":"2727","subfield":"Nephrology","fieldId":"27","field":"Medicine"},{"id":"T10516","name":"Parathyroid Disorders and Treatments","score":0,"subfieldId":"2727","subfield":"Nephrology","fieldId":"27","field":"Medicine"},{"id":"T10408","name":"Chronic Kidney Disease and Diabetes","score":0,"subfieldId":"2727","subfield":"Nephrology","fieldId":"27","field":"Medicine"}],"sources":["openalex"],"id":"openalex:W2009730157"},{"ids":{"openalex":"W1590499183","doi":"10.1038/1243","pmid":"9697696"},"title":"Alpha-2 macroglobulin is genetically associated with Alzheimer disease","authors":[{"name":"Deborah Blacker","id":"openalex:A5006645872","orcid":"https://orcid.org/0000-0001-6107-7376","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, USA","Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School"]},{"name":"Marsha Wilcox","id":"openalex:A5061873422","orcid":"https://orcid.org/0000-0001-8339-8621","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Epidemiology, Harvard School of Public Health"]},{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health"]},{"name":"L. Rodes","id":"openalex:A5082275052","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Neurology, Massachusetts General Hospital and Harvard Medical School","Genetics and Aging Unit, Massachusetts General Hospital and Harvard Medical School"]},{"name":"Steven M. Horvath","id":"openalex:A5109922336","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health"]},{"name":"Rodney C.P. Go","id":"openalex:A5110130320","affiliation":"University of Alabama at Birmingham","institutionId":"I32389192","institutionUrl":"https://ror.org/008s83205","affiliations":["Department of Epidemiology, University of Alabama at Birmingham"]},{"name":"Rodney T. Perry","id":"openalex:A5109178113","affiliation":"University of Alabama at Birmingham","institutionId":"I32389192","institutionUrl":"https://ror.org/008s83205","affiliations":["Department of Epidemiology, University of Alabama at Birmingham"]},{"name":"Bracie Watson","id":"openalex:A5067990689","affiliation":"University of Alabama at Birmingham","institutionId":"I32389192","institutionUrl":"https://ror.org/008s83205","affiliations":["Department of Epidemiology, University of Alabama at Birmingham"]},{"name":"Susan Spear Bassett","id":"openalex:A5111954692","affiliation":"Johns Hopkins University","institutionId":"I145311948","institutionUrl":"https://ror.org/00za53h95","affiliations":["Department of Psychiatry, Johns Hopkins University Medical Institutions"]},{"name":"Melvin G. McInnis","id":"openalex:A5013242137","orcid":"https://orcid.org/0000-0002-0375-6247","affiliation":"Johns Hopkins University","institutionId":"I145311948","institutionUrl":"https://ror.org/00za53h95","affiliations":["Department of Psychiatry, Johns Hopkins University Medical Institutions"]},{"name":"Marilyn Albert","id":"openalex:A5023514892","orcid":"https://orcid.org/0000-0002-8167-8985","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Neurology, Massachusetts General Hospital and Harvard Medical School","Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School"]},{"name":"Bradley T. Hyman","id":"openalex:A5068688487","orcid":"https://orcid.org/0000-0002-7959-9401","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Neurology, Massachusetts General Hospital and Harvard Medical School"]},{"name":"Rudolph E. Tanzi","id":"openalex:A5050004777","orcid":"https://orcid.org/0000-0002-7032-1454","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Neurology, Massachusetts General Hospital and Harvard Medical School","Genetics and Aging Unit, Massachusetts General Hospital and Harvard Medical School"],"corresponding":true}],"venue":"Nature Genetics","venueId":"S137905309","publisher":"Nature Portfolio","publisherId":"P4310319908","year":1998,"publishedDate":"1998-08-01","type":"article","citationCount":641,"referenceCount":37,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1038/1243","topics":[{"id":"T10086","name":"Alzheimer's disease research and treatments","score":0.9980000257492065,"subfieldId":"2737","subfield":"Physiology","fieldId":"27","field":"Medicine"},{"id":"T10009","name":"Dementia and Cognitive Impairment Research","score":0.0019000000320374966,"subfieldId":"2738","subfield":"Psychiatry and Mental health","fieldId":"27","field":"Medicine"},{"id":"T10261","name":"Genetic Associations and Epidemiology","score":0,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"}],"sources":["openalex"],"id":"openalex:W1590499183"},{"ids":{"openalex":"W2052071194","doi":"10.1159/000022918","pmid":"10782012"},"title":"A Unified Approach to Adjusting Association Tests for Population Admixture with Arbitrary Pedigree Structure and Arbitrary Missing Marker Information","abstract":"A general approach to family-based examinations of association between marker alleles and traits is proposed. The approach is based on computing p values by comparing test statistics for association to their conditional distributions given the minimal sufficient statistic under the null hypothesis for the genetic model, sampling plan and population admixture. The approach can be applied with any test statistic, so any kind of phenotype and multi-allelic markers may be examined, and covariates may be included in analyses. By virtue of the conditioning, the approach results in correct type I error probabilities regardless of population admixture, the true genetic model and the sampling strategy. An algorithm for computing the conditional distributions is described, and the results of the algorithm for configurations of nuclear families are presented. The algorithm is applicable with all pedigree structures and all patterns of missing marker allele information.","authors":[{"name":"Daniel Rabinowitz","id":"openalex:A5011792935","affiliation":"Columbia University","institutionId":"I78577930","institutionUrl":"https://ror.org/00hj8s172","affiliations":["Department of Statistics, Columbia University, New York, N.Y., USA. dan@stat.columbia.edu","Department of Statistics, Columbia University, New York, N.Y., and"]},{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health, Boston, Mass., USA"]}],"venue":"Human Heredity","venueId":"S97696616","publisher":"Karger Publishers","publisherId":"P4310317820","year":2000,"publishedDate":"2000-01-01","type":"article","citationCount":626,"referenceCount":15,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1159/000022918","topics":[{"id":"T10261","name":"Genetic Associations and Epidemiology","score":0.9775000214576721,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T11468","name":"Genetic Mapping and Diversity in Plants and Animals","score":0.008799999952316284,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T10751","name":"Forensic and Genetic Research","score":0.005200000014156103,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"}],"sources":["openalex"],"id":"openalex:W2052071194"},{"ids":{"openalex":"W2039979632","doi":"10.1080/01621459.1981.10477634"},"title":"Covariance Analysis of Censored Survival Data Using Log-Linear Analysis Techniques","abstract":"This paper unites two different fields, survival and contingency table analysis, in a single analytical framework based on the log-linear model. We demonstrate that many currently popular approaches to modeling survival data, including the approaches of Glasser (1967), Cox (1972), Breslow (1972, 1974), and Holford (1976), can be handled by using existing computer packages developed for the log-linear analysis of contingency table data. More important, we demonstrate that the log-linear modeling system used to characterize counted data structures directly characterizes survival data as well. Counted data methodologies for testing and estimation are also applicable here. Much of the theoretical basis for this work has been independently derived by Holford (1980) and Aitkin and Clayton (1980). The emphasis in this paper is not to develop new methodologies, but rather to present new uses and interpretations for already familiar methodologies.","authors":[{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Dept. of Biostatistics, School of Public Health, Harvard University, Boston, MA, 02115, USA","Department of Biostatistics, School of Public Health , Harvard University , Boston , MA , 02115 , USA"]},{"name":"Donald Olivier","id":"openalex:A5112054345","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Health Sciences Computing Facility, Harvard School of Public Health., USA","Health Sciences Computing Facility , Harvard School of Public Health. , USA"]}],"venue":"Journal of the American Statistical Association","venueId":"S4394736638","publisher":null,"year":1981,"publishedDate":"1981-06-01","type":"article","citationCount":580,"referenceCount":22,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1080/01621459.1981.10477634","topics":[{"id":"T10136","name":"Statistical Methods and Inference","score":0.46000000834465027,"subfieldId":"2613","subfield":"Statistics and Probability","fieldId":"26","field":"Mathematics"},{"id":"T10968","name":"Statistical Distribution Estimation and Applications","score":0.3402999937534332,"subfieldId":"2613","subfield":"Statistics and Probability","fieldId":"26","field":"Mathematics"},{"id":"T13500","name":"Probability and Statistical Research","score":0.16599999368190765,"subfieldId":"2613","subfield":"Statistics and Probability","fieldId":"26","field":"Mathematics"}],"sources":["openalex"],"id":"openalex:W2039979632"},{"ids":{"openalex":"W2155053630","doi":"10.1017/s0266462300008916","pmid":"2361819"},"title":"Some Statistical Methods for Combining Experimental Results","abstract":"Advances in science and technology are generally the product of multiple investigations. This article discusses statistical methods for combining empirical results from a series of different experiments or clinical investigations. We delineate the steps an assessor might take in combining data from different studies and provide references for topics not discussed in detail. The article reviews some of the most commonly used statistical techniques for combining results in the medical and social sciences. The expressionmeta-analysisrefers to the practice of using statistical methods to combine the outcomes of a series of different experiments or investigations. The terminology arises because a meta-analysis uses a study as the unit of observation, and, thus, the data point for each study is itself a statistical summary based on an analysis of primary data.","authors":[{"name":"Nan M. 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Mosteller","id":"openalex:A5109395807","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Harvard School of Public Health"]}],"venue":"International Journal of Technology Assessment in Health Care","venueId":"S83891556","publisher":"Cambridge University Press","publisherId":"P4310311721","year":1990,"publishedDate":"1990-01-01","type":"article","citationCount":568,"referenceCount":62,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1017/s0266462300008916","topics":[{"id":"T10206","name":"Meta-analysis and systematic reviews","score":0.9984999895095825,"subfieldId":"1804","subfield":"Statistics, Probability and Uncertainty","fieldId":"18","field":"Decision Sciences"},{"id":"T14374","name":"Statistical Methods in Epidemiology","score":0.0006000000284984708,"subfieldId":"2613","subfield":"Statistics and Probability","fieldId":"26","field":"Mathematics"},{"id":"T11235","name":"Statistical Methods in Clinical 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(MLB)","the Center for Population Genetics, School of Public Health, University of Illinois at Chicago (XX)","the Department of Biostatistics, Harvard School of Public Health, Boston, MA (NL)","the Division of Preventive Medicine, Department of Medicine, Brigham and Women Hospital, Harvard Medical School, Boston, MA (TN)","the Maine Center for Osteoporosis Research and Education, St Joseph Hospital, Bangor, ME (CJR)","the San Francisco Coordinating Center, University of California, San Francisco, CA (SRC)"]},{"name":"Scott Alan Venners","id":"openalex:A5041376144","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["Anhui Medical University, Institute of Medicine, Anhui, China (ZL and XX)","From the Program for Population Genetics, Harvard School of Public Health, Boston, MA (Y-HH, SAV, HAT, YF, and JDB)","the Beth Israel Deaconess Medical Center, Boston, MA (MLB)","the Center for Population Genetics, School of Public Health, University of Illinois at Chicago (XX)","the Department of Biostatistics, Harvard School of Public Health, Boston, MA (NL)","the Division of Preventive Medicine, Department of Medicine, Brigham and Women Hospital, Harvard Medical School, Boston, MA (TN)","the Maine Center for Osteoporosis Research and Education, St Joseph Hospital, Bangor, ME (CJR)","the San Francisco Coordinating Center, University of California, San Francisco, CA (SRC)"]},{"name":"Henry A Terwedow","id":"openalex:A5012948692","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["Anhui Medical University, Institute of Medicine, Anhui, China (ZL and XX)","From the Program for Population Genetics, Harvard School of Public Health, Boston, MA (Y-HH, SAV, HAT, YF, and JDB)","the Beth Israel Deaconess Medical Center, Boston, MA (MLB)","the Center for Population Genetics, School of Public Health, University of Illinois at Chicago (XX)","the Department of Biostatistics, Harvard School of Public Health, Boston, MA (NL)","the Division of Preventive Medicine, Department of Medicine, Brigham and Women Hospital, Harvard Medical School, Boston, MA (TN)","the Maine Center for Osteoporosis Research and Education, St Joseph Hospital, Bangor, ME (CJR)","the San Francisco Coordinating Center, University of California, San Francisco, CA (SRC)"]},{"name":"Yan Feng","id":"openalex:A5103272437","orcid":"https://orcid.org/0000-0002-6441-4179","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["Anhui Medical University, Institute of Medicine, Anhui, China (ZL and XX)","From the Program for Population Genetics, Harvard School of Public Health, Boston, MA (Y-HH, SAV, HAT, YF, and JDB)","the Beth Israel Deaconess Medical Center, Boston, MA (MLB)","the Center for Population Genetics, School of Public Health, University of Illinois at 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Chicago (XX)","the Department of Biostatistics, Harvard School of Public Health, Boston, MA (NL)","the Division of Preventive Medicine, Department of Medicine, Brigham and Women Hospital, Harvard Medical School, Boston, MA (TN)","the Maine Center for Osteoporosis Research and Education, St Joseph Hospital, Bangor, ME (CJR)","the San Francisco Coordinating Center, University of California, San Francisco, CA (SRC)"]},{"name":"Zhiping Li","id":"openalex:A5100669380","orcid":"https://orcid.org/0000-0001-7513-8272","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["Anhui Medical University, Institute of Medicine, Anhui, China (ZL and XX)","From the Program for Population Genetics, Harvard School of Public Health, Boston, MA (Y-HH, SAV, HAT, YF, and JDB)","the Beth Israel Deaconess Medical Center, Boston, MA (MLB)","the Center for Population Genetics, School of Public Health, University of Illinois at Chicago (XX)","the Department of Biostatistics, Harvard School of Public Health, Boston, MA (NL)","the Division of Preventive Medicine, Department of Medicine, Brigham and Women Hospital, Harvard Medical School, Boston, MA (TN)","the Maine Center for Osteoporosis Research and Education, St Joseph Hospital, Bangor, ME (CJR)","the San Francisco Coordinating Center, University of California, San Francisco, CA (SRC)"]},{"name":"Nan M. 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(SRC)"]},{"name":"Clifford james Rosen","id":"openalex:A5047044979","orcid":"https://orcid.org/0000-0003-3436-8199","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["Anhui Medical University, Institute of Medicine, Anhui, China (ZL and XX)","From the Program for Population Genetics, Harvard School of Public Health, Boston, MA (Y-HH, SAV, HAT, YF, and JDB)","the Beth Israel Deaconess Medical Center, Boston, MA (MLB)","the Center for Population Genetics, School of Public Health, University of Illinois at Chicago (XX)","the Department of Biostatistics, Harvard School of Public Health, Boston, MA (NL)","the Division of Preventive Medicine, Department of Medicine, Brigham and Women Hospital, Harvard Medical School, Boston, MA (TN)","the Maine Center for Osteoporosis Research and Education, St Joseph Hospital, Bangor, ME (CJR)","the San Francisco Coordinating Center, University of California, San 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Francisco, CA (SRC)"],"corresponding":true}],"venue":"American Journal of Clinical Nutrition","venueId":"S71285955","publisher":"Elsevier BV","publisherId":"P4310320990","year":2006,"publishedDate":"2006-01-01","type":"article","citationCount":537,"referenceCount":39,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.1093/ajcn/83.1.146","oaUrl":"https://doi.org/10.1093/ajcn/83.1.146","topics":[{"id":"T10071","name":"Bone health and osteoporosis research","score":0.9994999766349792,"subfieldId":"2732","subfield":"Orthopedics and Sports Medicine","fieldId":"27","field":"Medicine"},{"id":"T11655","name":"HIV-related health complications and treatments","score":0,"subfieldId":"2711","subfield":"Emergency Medicine","fieldId":"27","field":"Medicine"},{"id":"T12279","name":"Body Composition Measurement 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Cullen","id":"openalex:A5111941090"},{"name":"Nan M. 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DiVito","id":"openalex:A5113624684","affiliation":"Massachusetts General Hospital","institutionId":"I4210087915","institutionUrl":"https://ror.org/002pd6e78","affiliations":["Genetics and Aging Research Unit, Mass General Institute for Neurodegenerative Disease (MIND), Department of Neurology, Massachusetts General Hospital, Charlestown, MA 02129, USA"]},{"name":"Iuliana Ionita","id":"openalex:A5008054046","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA"]},{"name":"Hongyu Jiang","id":"openalex:A5024214452","orcid":"https://orcid.org/0000-0001-5774-6258","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA"]},{"name":"Nan M. 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Wagner","id":"openalex:A5023581135","orcid":"https://orcid.org/0000-0003-4674-9474","affiliation":"TorreyPines Therapeutics, La Jolla, CA 92037, USA","affiliations":["TorreyPines Therapeutics, La Jolla, CA 92037, USA"]},{"name":"Deborah Blacker","id":"openalex:A5006645872","orcid":"https://orcid.org/0000-0001-6107-7376","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA","Gerontology Research Unit, Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA"]},{"name":"Kathrin Becker","id":"openalex:A5076658354","orcid":"https://orcid.org/0000-0003-4231-2590","affiliation":"TorreyPines Therapeutics, La Jolla, CA 92037, USA","affiliations":["TorreyPines Therapeutics, La Jolla, CA 92037, USA"]},{"name":"Rudolph E. 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No substantial dose-related increase was noted for basal-cell carcinoma. The dose-dependent risk of cutaneous squamous-cell carcinoma suggests that PUVA can act as an independent carcinogen. In our study, morbidity associated with these tumors has been limited, but further follow-up is needed. Meanwhile, patients treated with PUVA should be followed closely for the possible development of cutaneous squamous-cell carcinoma.","authors":[{"name":"Robert S. Stern","id":"openalex:A5045699592","orcid":"https://orcid.org/0000-0001-5491-149X"},{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234"},{"name":"John W. Melski","id":"openalex:A5071103918"},{"name":"John Albert Parrish","id":"openalex:A5111705571"},{"name":"Thomas B. Fitzpatrick","id":"openalex:A5104063886"},{"name":"Howard L. 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Colditz","id":"openalex:A5031201037","orcid":"https://orcid.org/0000-0002-7307-0291","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["Department of Medicine, Department of Epidemiology, Channing Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA","Harvard University"]},{"name":"KE Peterson","id":"openalex:A5111880166","affiliation":"Department of Maternal and Child Health and Department of Nutrition, Boston, MA, USA","affiliations":["Department of Maternal and Child Health and Department of Nutrition, Boston, MA, USA","Department of Maternal and Child Health and Department of Nutrition, Boston, USA"]},{"name":"Nan M. 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Neale","id":"openalex:A5015235673","orcid":"https://orcid.org/0000-0003-1513-6077","affiliation":"Broad Institute","institutionId":"I107606265","institutionUrl":"https://ror.org/05a0ya142","affiliations":["Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts","Department of Psychiatry, SUNY Upstate Medical University, Syracuse, New York","Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, London, UK","The Broad Institute of Harvard and MIT, Cambridge, Massachusetts"]},{"name":"Barbara Franke","id":"openalex:A5038874292","orcid":"https://orcid.org/0000-0003-4375-6572","affiliation":"Radboud University Nijmegen","institutionId":"I145872427","institutionUrl":"https://ror.org/016xsfp80","affiliations":["Department of Human Genetics, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands","Department of Psychiatry, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands"]},{"name":"Richard Anney","id":"openalex:A5023294157","orcid":"https://orcid.org/0000-0002-6083-407X","affiliation":"St. James's Hospital","institutionId":"I4210154619","institutionUrl":"https://ror.org/04c6bry31","affiliations":["Department of Psychiatry, Trinity Centre for Health Sciences, St. James's Hospital, Dublin, Ireland"]},{"name":"Kaixin Zhou","id":"openalex:A5107119585","affiliation":"King's College London","institutionId":"I183935753","institutionUrl":"https://ror.org/0220mzb33","affiliations":["Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, London, UK"]},{"name":"Julian Maller","id":"openalex:A5026130275","orcid":"https://orcid.org/0000-0002-1565-9559","affiliation":"Centre for Human Genetics","institutionId":"I1336263701","institutionUrl":"https://ror.org/01rjnta51","affiliations":["Wellcome Trust Centre for Human Genetics, Oxford University, Oxford, UK"]},{"name":"Alejandro Arias Vásquez","id":"openalex:A5029619884","orcid":"https://orcid.org/0000-0002-4786-0169","affiliation":"Radboud University Nijmegen","institutionId":"I145872427","institutionUrl":"https://ror.org/016xsfp80","affiliations":["Department of Human Genetics, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands","Department of Psychiatry, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands"]},{"name":"Wai Chen","id":"openalex:A5044566270","orcid":"https://orcid.org/0000-0002-0477-7883","affiliation":"King's College London","institutionId":"I183935753","institutionUrl":"https://ror.org/0220mzb33","affiliations":["Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, London, UK"]},{"name":"Philip J. Asherson","id":"openalex:A5017195801","orcid":"https://orcid.org/0000-0003-2667-2254","affiliation":"King's College London","institutionId":"I183935753","institutionUrl":"https://ror.org/0220mzb33","affiliations":["Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, London, UK"]},{"name":"Jan K. Buitelaar","id":"openalex:A5009149756","orcid":"https://orcid.org/0000-0001-8288-7757","affiliation":"Radboud University Nijmegen","institutionId":"I145872427","institutionUrl":"https://ror.org/016xsfp80","affiliations":["Department of Psychiatry, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands"]},{"name":"Tobias Banaschewski","id":"openalex:A5016008616","orcid":"https://orcid.org/0000-0003-4595-1144","affiliation":"Heidelberg University","institutionId":"I223822909","institutionUrl":"https://ror.org/038t36y30","affiliations":["Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany"]},{"name":"Richard P. Ebstein","id":"openalex:A5012156936","orcid":"https://orcid.org/0000-0002-3626-541X","affiliation":"Geha Mental Health Center","institutionId":"I4210143993","institutionUrl":"https://ror.org/03tp0ty93","affiliations":["Geha MHC, Petach-Tikva, Israel"]},{"name":"Michael Gill","id":"openalex:A5106497970","orcid":"https://orcid.org/0000-0003-0206-5337","affiliation":"St. James's Hospital","institutionId":"I4210154619","institutionUrl":"https://ror.org/04c6bry31","affiliations":["Department of Psychiatry, Trinity Centre for Health Sciences, St. James's Hospital, Dublin, Ireland"]},{"name":"Ana Miranda","id":"openalex:A5032779862","orcid":"https://orcid.org/0000-0001-6998-5686","affiliation":"Universitat de València","institutionId":"I16097986","institutionUrl":"https://ror.org/043nxc105","affiliations":["Department of Developmental and Educational Psychology, University of Valencia, Valencia, Spain"]},{"name":"Fernando Mulas","id":"openalex:A5110024575","affiliation":"Hospital Universitari i Politècnic La Fe","institutionId":"I3019010403","institutionUrl":"https://ror.org/01ar2v535","affiliations":["Department of Neuropaediatrics, La Fe University Hospital, Valencia, Spain"]},{"name":"Robert D. Oades","id":"openalex:A5108099223","affiliation":"University Clinic for Child and Adolescent Psychiatry, Essen, Germany","affiliations":["University Clinic for Child and Adolescent Psychiatry, Essen, Germany"]},{"name":"Herbert Roeyers","id":"openalex:A5028229633","orcid":"https://orcid.org/0000-0002-4895-583X","affiliation":"Ghent University","institutionId":"I32597200","institutionUrl":"https://ror.org/00cv9y106","affiliations":["Ghent University, Dunantlaan, Ghent, Belgium"]},{"name":"Aribert Rothenberger","id":"openalex:A5062181343","orcid":"https://orcid.org/0000-0002-2367-9197","affiliation":"University of Göttingen","institutionId":"I74656192","institutionUrl":"https://ror.org/01y9bpm73","affiliations":["Child and Adolescent Psychiatry, University of Gottingen, Gottingen, Germany"]},{"name":"Joseph A. Sergeant","id":"openalex:A5108541668","orcid":"https://orcid.org/0000-0003-1703-7080","affiliation":"Vrije Universiteit Amsterdam","institutionId":"I865915315","institutionUrl":"https://ror.org/008xxew50","affiliations":["Vrije Universiteit, De Boelelaan, Amsterdam, The Netherlands"]},{"name":"Edmund J. S. 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Daly","id":"openalex:A5107596541","orcid":"https://orcid.org/0000-0002-0949-8752","affiliation":"Broad Institute","institutionId":"I107606265","institutionUrl":"https://ror.org/05a0ya142","affiliations":["Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts","The Broad Institute of Harvard and MIT, Cambridge, Massachusetts"]},{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Harvard School of Public Health, Boston, Massachusetts"]},{"name":"Christoph Lange","id":"openalex:A5002736890","orcid":"https://orcid.org/0000-0003-2620-1030","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts","Harvard School of Public Health, Boston, Massachusetts"]},{"name":"Stephen V. 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Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts"]}],"venue":"Genetic Epidemiology","venueId":"S136352176","publisher":"Wiley","publisherId":"P4310320595","year":2003,"publishedDate":"2003-12-16","type":"article","citationCount":391,"referenceCount":27,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1002/gepi.10295","topics":[{"id":"T10261","name":"Genetic Associations and Epidemiology","score":0.9965999722480774,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T11468","name":"Genetic Mapping and Diversity in Plants and Animals","score":0.0010999999940395355,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T10751","name":"Forensic and Genetic Research","score":0.0006000000284984708,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"}],"sources":["openalex"],"id":"openalex:W2098611654"},{"ids":{"openalex":"W2154083724","doi":"10.1001/archpedi.153.9.975","pmid":"10482216"},"title":"Impact of a School-Based Interdisciplinary Intervention on Diet and Physical Activity Among Urban Primary School Children","abstract":"$2e","authors":[{"name":"Steven L. Gortmaker","id":"openalex:A5074763341","orcid":"https://orcid.org/0000-0003-4904-9377","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Health and Social Behavior, Harvard School of Public Health, Boston, Mass. 02115, USA"]},{"name":"Lilian W. Y. Cheung"},{"name":"Karen E. Peterson","id":"openalex:A5082746757","orcid":"https://orcid.org/0000-0003-0471-1427"},{"name":"G Chomitz","id":"openalex:A5013463496"},{"name":"J H Cradle","id":"openalex:A5035903631"},{"name":"Hank Dart","id":"openalex:A5063410935","orcid":"https://orcid.org/0000-0002-2668-5920"},{"name":"Mary Kay Fox","id":"openalex:A5004225516","orcid":"https://orcid.org/0000-0001-9378-2619"},{"name":"R B Bullock","id":"openalex:A5056624431"},{"name":"Arthur M. Sobol","id":"openalex:A5010316983"},{"name":"Graham A. Colditz","id":"openalex:A5031201037","orcid":"https://orcid.org/0000-0002-7307-0291"},{"name":"Alison E. Field"},{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234"}],"venue":"Archives of Pediatrics and Adolescent Medicine","venueId":"S4210192535","publisher":"American Medical Association","publisherId":"P4310320259","year":1999,"publishedDate":"1999-09-01","type":"article","citationCount":386,"referenceCount":41,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1001/archpedi.153.9.975","topics":[{"id":"T10010","name":"Obesity, Physical Activity, Diet","score":0.9301999807357788,"subfieldId":"2739","subfield":"Public Health, Environmental and Occupational Health","fieldId":"27","field":"Medicine"},{"id":"T12831","name":"School Health and Nursing Education","score":0.053300000727176666,"subfieldId":"3616","subfield":"Speech and Hearing","fieldId":"36","field":"Health Professions"},{"id":"T12873","name":"Nutrition, Health and Food Behavior","score":0.009800000116229057,"subfieldId":"2916","subfield":"Nutrition and Dietetics","fieldId":"29","field":"Nursing"}],"sources":["openalex"],"id":"openalex:W2154083724"},{"ids":{"openalex":"W1983279407","doi":"10.1002/1097-0142(19940601)73:11<2759::aid-cncr2820731118>3.0.co;2-c","pmid":"8194017"},"title":"The carcinogenic risk of treatments for severe psoriasis","abstract":"$2f","authors":[{"name":"Robert S. 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Electronic address: remhc@channing.harvard.edu","Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA"],"corresponding":true},{"name":"Merry‐Lynn McDonald","id":"openalex:A5029870208","orcid":"https://orcid.org/0000-0002-2647-9422","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA"]},{"name":"Xiaobo Zhou","id":"openalex:A5053033228","orcid":"https://orcid.org/0000-0002-7127-2869","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA","Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA"]},{"name":"Manuel Mattheisen","id":"openalex:A5091880715","orcid":"https://orcid.org/0000-0002-8442-493X","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA","Harvard School of Public Health, Boston, MA, USA"]},{"name":"Peter J. 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Graham Barr","id":"openalex:A5070233463","orcid":"https://orcid.org/0009-0006-9613-4426","affiliation":"Columbia University","institutionId":"I78577930","institutionUrl":"https://ror.org/00hj8s172","affiliations":["Department of Medicine, College of Physicians and Surgeons, Mailman School of Public Health, Columbia University, New York, NY, USA","Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA"]},{"name":"Elizabeth Anne Regan","id":"openalex:A5011343670","orcid":"https://orcid.org/0000-0003-0624-9184","affiliation":"Colorado School of Public Health","institutionId":"I151808059","institutionUrl":"https://ror.org/005x9g035","affiliations":["National Jewish Health, Denver, CO, USA","Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, CO, USA"]},{"name":"Barry J. 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Beaty","id":"openalex:A5025950806","orcid":"https://orcid.org/0000-0002-7644-1705","affiliation":"Johns Hopkins University","institutionId":"I145311948","institutionUrl":"https://ror.org/00za53h95","affiliations":["Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA"]}],"venue":"The Lancet Respiratory Medicine","venueId":"S4210192742","publisher":"Elsevier BV","publisherId":"P4310320990","year":2014,"publishedDate":"2014-02-06","type":"article","citationCount":347,"referenceCount":112,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.1016/s2213-2600(14)70002-5","oaUrl":"https://www.ncbi.nlm.nih.gov/pmc/articles/4176924","topics":[{"id":"T10143","name":"Chronic Obstructive Pulmonary Disease (COPD) Research","score":0.989300012588501,"subfieldId":"2740","subfield":"Pulmonary and Respiratory Medicine","fieldId":"27","field":"Medicine"},{"id":"T10261","name":"Genetic Associations and Epidemiology","score":0.00989999994635582,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T10060","name":"Smoking Behavior and Cessation","score":0.00009999999747378752,"subfieldId":"2737","subfield":"Physiology","fieldId":"27","field":"Medicine"}],"sources":["openalex"],"id":"openalex:W2005623173"},{"ids":{"openalex":"W1974345349","doi":"10.1002/(sici)1097-0258(19970215)16:3<239::aid-sim483>3.0.co;2-x","pmid":"9004395"},"title":"MIXTURE MODELS FOR THE JOINT DISTRIBUTION OF REPEATED MEASURES AND EVENT TIMES","abstract":"Many long-term clinical trials collect both a vector of repeated measurements and an event time on each subject; often, the two outcomes are dependent. One example is the use of surrogate markers to predict disease onset or survival. Another is longitudinal trials which have outcome-related dropout. We describe a mixture model for the joint distribution which accommodates incomplete repeated measures and right-censored event times, and provide methods for full maximum likelihood estimation. The methods are illustrated through analysis of data from a clinical trial for a new schizophrenia therapy; in the trial, dropout time is closely related to outcome, and the dropout process differs between treatments. The parameter estimates from the model are used to make a treatment comparison after adjusting for the effects of dropout. An added benefit of the analysis is that it permits using the repeated measures to increase efficiency of estimates of the event time distribution.","authors":[{"name":"Joseph W. Hogan","id":"openalex:A5061915997","orcid":"https://orcid.org/0000-0001-7959-7361","affiliation":"Brown University","institutionId":"I27804330","institutionUrl":"https://ror.org/05gq02987","affiliations":["Center for Statistical Sciences, Brown University, Providence, RI 02912, USA","Center for Statistical Sciences, Brown University Providence, RI 02912, USA"]},{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, U.S.A","Department of Biostatistics Harvard School of Public Health 677 Huntington Avenue Boston MA 02115 U.S.A"]}],"venue":"Statistics in Medicine","venueId":"S55189604","publisher":"Wiley","publisherId":"P4310320595","year":1997,"publishedDate":"1997-02-15","type":"article","citationCount":340,"referenceCount":0,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1002/(sici)1097-0258(19970215)16:3<239::aid-sim483>3.0.co;2-x","topics":[{"id":"T10243","name":"Statistical Methods and Bayesian Inference","score":0.8291000127792358,"subfieldId":"2613","subfield":"Statistics and Probability","fieldId":"26","field":"Mathematics"},{"id":"T11235","name":"Statistical Methods in Clinical Trials","score":0.11029999703168869,"subfieldId":"2613","subfield":"Statistics and Probability","fieldId":"26","field":"Mathematics"},{"id":"T11901","name":"Bayesian Methods and Mixture Models","score":0.04149999842047691,"subfieldId":"1702","subfield":"Artificial Intelligence","fieldId":"17","field":"Computer Science"}],"sources":["openalex"],"id":"openalex:W1974345349"},{"ids":{"openalex":"W1999154462","doi":"10.1093/biomet/80.1.141"},"title":"A Likelihood-Based Method for Analysing Longitudinal Binary Responses","abstract":"In this paper, we discuss a likelihood-based method for analysing correlated binary responses based on a multivariate model. It is related to the pseudo-maximum likelihood approach suggested recently by Zhao & Prentice (1990). Their parameterization results in a simple pairwise model, in which the association between responses is modelled in terms of correlations, while the present paper uses conditional log odds-ratios. With this approach, higher-order associations can be incorporated in a natural way. One important advantage of this parameterization is that the maximum likelihood estimates of the marginal mean parameters are robust to misspecification of the time dependence. We describe an iterative two-stage procedure for obtaining the maximum likelihood estimates. Two examples are presented to illustrate this methodology.","authors":[{"name":"Garrett M. Fitzmaurice","id":"openalex:A5055526341","orcid":"https://orcid.org/0000-0002-2265-8810","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health 677 Huntington Avenue, Boston, Massachusettus 02115, U.S.A","Department of Biostatistics, Harvard School of Public Health677 Huntington Avenue, Boston, Massachusettus 02115, U.S.A"]},{"name":"Nan M. 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Leighton","id":"openalex:A5108381053"}],"venue":"Archives of General Psychiatry","venueId":"S126359496","publisher":"American Medical Association","publisherId":"P4310320259","year":2000,"publishedDate":"2000-03-01","type":"article","citationCount":318,"referenceCount":54,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1001/archpsyc.57.3.209","topics":[{"id":"T10272","name":"Mental Health Treatment and Access","score":0.986299991607666,"subfieldId":"3207","subfield":"Social Psychology","fieldId":"32","field":"Psychology"},{"id":"T10853","name":"Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes","score":0.00279999990016222,"subfieldId":"3205","subfield":"Experimental and Cognitive Psychology","fieldId":"32","field":"Psychology"},{"id":"T10182","name":"Child and Adolescent Psychosocial and Emotional Development","score":0.0019000000320374966,"subfieldId":"3203","subfield":"Clinical Psychology","fieldId":"32","field":"Psychology"}],"sources":["openalex"],"id":"openalex:W2124480078"},{"ids":{"openalex":"W1981132558","doi":"10.1086/381563","pmid":"14740322"},"title":"PBAT: Tools for Family-Based Association Studies","authors":[{"name":"Christoph Lange","id":"openalex:A5002736890","orcid":"https://orcid.org/0000-0003-2620-1030","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health, Boston"],"corresponding":true},{"name":"Dawn Lisa DeMeo","id":"openalex:A5051093934","orcid":"https://orcid.org/0000-0001-9653-0636","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["Department of Medicine, Brigham and Women’s Hospital, Boston","Department of Medicine, Brigham and Women's Hospital, Boston"]},{"name":"Edwin K. 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Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health, Boston"]}],"venue":"The American Journal of Human Genetics","venueId":"S134425043","publisher":"Elsevier BV","publisherId":"P4310320990","year":2004,"publishedDate":"2004-01-26","type":"article","citationCount":268,"referenceCount":18,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.1086/381563","oaUrl":"https://doi.org/10.1086/381563","topics":[{"id":"T10261","name":"Genetic Associations and Epidemiology","score":0.9854000210762024,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T11468","name":"Genetic Mapping and Diversity in Plants and Animals","score":0.006000000052154064,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T10012","name":"Genetic diversity and population structure","score":0.00139999995008111,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"}],"sources":["openalex"],"id":"openalex:W1981132558"},{"ids":{"openalex":"W2002036523","doi":"10.1086/316895","pmid":"11058432"},"title":"Family-Based Tests of Association in the Presence of Linkage","authors":[{"name":"Stephen L. Lake","id":"openalex:A5109202089","orcid":"https://orcid.org/0009-0001-8154-8515","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health, Harvard University, Boston, MA 02115, USA. slake@hsph.harvard.edu","Departments of Biostatistics Harvard University, Boston"],"corresponding":true},{"name":"Deborah Blacker","id":"openalex:A5006645872","orcid":"https://orcid.org/0000-0001-6107-7376","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Harvard University, Boston","Departments of Epidemiology, Harvard School of Public Health Harvard University, Boston"]},{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Departments of Biostatistics Harvard University, Boston"]}],"venue":"The American Journal of Human Genetics","venueId":"S134425043","publisher":"Elsevier BV","publisherId":"P4310320990","year":2000,"publishedDate":"2000-12-01","type":"article","citationCount":267,"referenceCount":44,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.1086/316895","oaUrl":"https://doi.org/10.1086/316895","topics":[{"id":"T10261","name":"Genetic Associations and Epidemiology","score":0.9878000020980835,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T11468","name":"Genetic Mapping and Diversity in Plants and Animals","score":0.008299999870359898,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T10751","name":"Forensic and Genetic Research","score":0.000699999975040555,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"}],"sources":["openalex"],"id":"openalex:W2002036523"},{"ids":{"openalex":"W2113747602","doi":"10.1002/ajmg.b.30866","pmid":"18980221"},"title":"Genome‐wide association scan of attention deficit hyperactivity disorder","abstract":"Results of behavioral genetic and molecular genetic studies have converged to suggest that genes substantially contribute to the development of attention deficit/hyperactivity disorder (ADHD), a common disorder with an onset in childhood. Yet, despite numerous linkage and candidate gene studies, strongly consistent and replicable association has eluded detection. To search for ADHD susceptibility genes, we genotyped approximately 600,000 SNPs in 958 ADHD affected family trios. After cleaning the data, we analyzed 438,784 SNPs in 2,803 individuals comprising 909 complete trios using ADHD diagnosis as phenotype. We present the initial TDT findings as well as considerations for cleaning family-based TDT data. None of the SNP association tests achieved genome-wide significance, indicating that larger samples may be required to identify risk loci for ADHD. We additionally identify a systemic bias in family-based association, and suggest that variable missing genotype rates may be the source of this bias.","authors":[{"name":"Benjamin M. Neale","id":"openalex:A5015235673","orcid":"https://orcid.org/0000-0003-1513-6077","affiliation":"Broad Institute","institutionId":"I107606265","institutionUrl":"https://ror.org/05a0ya142","affiliations":["Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, UK","Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts","Department of Psychiatry, SUNY Upstate Medical University, Syracuse, New York","The Broad Institute of Harvard and MIT, Cambridge, Massachusetts"]},{"name":"Jessica Ann Lasky-Su","id":"openalex:A5008569720","orcid":"https://orcid.org/0000-0001-6236-4705","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts","Department of Psychiatry, SUNY Upstate Medical University, Syracuse, New York"]},{"name":"Richard Anney","id":"openalex:A5023294157","orcid":"https://orcid.org/0000-0002-6083-407X","affiliation":"St. James's Hospital","institutionId":"I4210154619","institutionUrl":"https://ror.org/04c6bry31","affiliations":["Department of Psychiatry, Trinity Centre for Health Sciences, St. James's Hospital, Dublin, Ireland"]},{"name":"Barbara Franke","id":"openalex:A5038874292","orcid":"https://orcid.org/0000-0003-4375-6572","affiliation":"Radboud University Nijmegen","institutionId":"I145872427","institutionUrl":"https://ror.org/016xsfp80","affiliations":["Department of Human Genetics, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands","Department of Psychiatry, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands"]},{"name":"Kaixin Zhou","id":"openalex:A5107119585","affiliation":"King's College London","institutionId":"I183935753","institutionUrl":"https://ror.org/0220mzb33","affiliations":["Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, UK"]},{"name":"Julian Maller","id":"openalex:A5026130275","orcid":"https://orcid.org/0000-0002-1565-9559","affiliation":"University of Oxford","institutionId":"I40120149","institutionUrl":"https://ror.org/052gg0110","affiliations":["Department of Statistics, Oxford University, Oxford, UK"]},{"name":"Alejandro Arias Vásquez","id":"openalex:A5029619884","orcid":"https://orcid.org/0000-0002-4786-0169","affiliation":"Radboud University Nijmegen","institutionId":"I145872427","institutionUrl":"https://ror.org/016xsfp80","affiliations":["Department of Human Genetics, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands","Department of Psychiatry, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands"]},{"name":"Philip J. Asherson","id":"openalex:A5017195801","orcid":"https://orcid.org/0000-0003-2667-2254","affiliation":"King's College London","institutionId":"I183935753","institutionUrl":"https://ror.org/0220mzb33","affiliations":["Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, UK"]},{"name":"Wai Chen","id":"openalex:A5044566270","orcid":"https://orcid.org/0000-0002-0477-7883","affiliation":"King's College London","institutionId":"I183935753","institutionUrl":"https://ror.org/0220mzb33","affiliations":["Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, UK"]},{"name":"Tobias Banaschewski","id":"openalex:A5016008616","orcid":"https://orcid.org/0000-0003-4595-1144","affiliation":"Heidelberg University","institutionId":"I223822909","institutionUrl":"https://ror.org/038t36y30","affiliations":["Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany"]},{"name":"Jan K. 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Ebstein","id":"openalex:A5012156936","orcid":"https://orcid.org/0000-0002-3626-541X","affiliation":"Geha Mental Health Center","institutionId":"I4210143993","institutionUrl":"https://ror.org/03tp0ty93","affiliations":["Geha MHC, Petach-Tikva, Israel"]},{"name":"Michael Gill","id":"openalex:A5106497970","orcid":"https://orcid.org/0000-0003-0206-5337","affiliation":"St. James's Hospital","institutionId":"I4210154619","institutionUrl":"https://ror.org/04c6bry31","affiliations":["Department of Psychiatry, Trinity Centre for Health Sciences, St. James's Hospital, Dublin, Ireland"]},{"name":"Ana Miranda","id":"openalex:A5032779862","orcid":"https://orcid.org/0000-0001-6998-5686","affiliation":"Universitat de València","institutionId":"I16097986","institutionUrl":"https://ror.org/043nxc105","affiliations":["Department of Developmental and Educational Psychology, University of Valencia, Valencia, Spain"]},{"name":"Robert D. Oades","id":"openalex:A5108099223","affiliation":"University Clinic for Child and Adolescent Psychiatry, Essen, Germany","affiliations":["University Clinic for Child and Adolescent Psychiatry, Essen, Germany"]},{"name":"Herbert Roeyers","id":"openalex:A5028229633","orcid":"https://orcid.org/0000-0002-4895-583X","affiliation":"Ghent University","institutionId":"I32597200","institutionUrl":"https://ror.org/00cv9y106","affiliations":["Ghent University, Dunantlaan, Ghent, Belgium"]},{"name":"Aribert Rothenberger","id":"openalex:A5062181343","orcid":"https://orcid.org/0000-0002-2367-9197","affiliation":"University of Göttingen","institutionId":"I74656192","institutionUrl":"https://ror.org/01y9bpm73","affiliations":["Child and Adolescent Psychiatry, University of Gottingen, Gottingen, Germany"]},{"name":"Joseph A. 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Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Harvard School of Public Health, Boston, Massachusetts"]},{"name":"Christoph Lange","id":"openalex:A5002736890","orcid":"https://orcid.org/0000-0003-2620-1030","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts","Harvard School of Public Health, Boston, Massachusetts"]},{"name":"Mark J. 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Planet Health sessions were included within existing curricula using classroom teachers in 4 major subjects and physical education. Sessions focused on decreasing television viewing, decreasing consumption of high-fat foods, increasing fruit and vegetable intake, and increasing moderate and vigorous physical activity.","authors":[{"name":"Steven L. Gortmaker","id":"openalex:A5074763341","orcid":"https://orcid.org/0000-0003-4904-9377","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Health and Social Behavior, Harvard School of Public Health, Boston, Mass 02115, USA"]},{"name":"Karen E. Peterson","id":"openalex:A5082746757","orcid":"https://orcid.org/0000-0003-0471-1427"},{"name":"Jean L. Wiecha","id":"openalex:A5043789300"},{"name":"Arthur M. 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These models have been developed for either longitudinal studies with dropout or for survival studies in which a random, time-varying covariate is measured repeatedly across time. When inference about the longitudinal variable is of interest, event times are treated as covariates and are often incomplete due to censoring. If survival or event time is the primary endpoint, repeated outcomes observed prior to the event are viewed as covariates; this covariate process is often incomplete, measured with error, or observed at unscheduled times during the study. We review several models which are used to handle incomplete response and covariate data in both survival and longitudinal studies.","authors":[{"name":"Joseph W. Hogan","id":"openalex:A5061915997","orcid":"https://orcid.org/0000-0001-7959-7361","affiliation":"Brown University","institutionId":"I27804330","institutionUrl":"https://ror.org/05gq02987","affiliations":["Center for Statistical Sciences, Brown University, Providence, RI 02192, USA","Center for Statistical Sciences, Brown University, Box G-H, Providence, RI 02192, U.S.A"]},{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue Boston, MA 02115, U.S.A","Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115 U.S.A"]}],"venue":"Statistics in Medicine","venueId":"S55189604","publisher":"Wiley","publisherId":"P4310320595","year":1997,"publishedDate":"1997-02-15","type":"article","citationCount":243,"referenceCount":0,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1002/(sici)1097-0258(19970215)16:3<259::aid-sim484>3.0.co;2-s","topics":[{"id":"T10243","name":"Statistical Methods and Bayesian Inference","score":0.9932000041007996,"subfieldId":"2613","subfield":"Statistics and Probability","fieldId":"26","field":"Mathematics"},{"id":"T11235","name":"Statistical Methods in Clinical Trials","score":0.003700000001117587,"subfieldId":"2613","subfield":"Statistics and Probability","fieldId":"26","field":"Mathematics"},{"id":"T13500","name":"Probability and Statistical Research","score":0.0005000000237487257,"subfieldId":"2613","subfield":"Statistics and Probability","fieldId":"26","field":"Mathematics"}],"sources":["openalex"],"id":"openalex:W2103451607"},{"ids":{"openalex":"W2031508870","doi":"10.4088/jcp.v69n0213","pmid":"18348600"},"title":"Co-Occurrence of Binge Eating Disorder With Psychiatric and Medical Disorders","abstract":"$31","authors":[{"name":"Kristin N. Javaras","id":"openalex:A5034561165","orcid":"https://orcid.org/0000-0003-0885-2331","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Department of Biostatistics, Harvard School of Public Health, Boston, Mass., USA. javaras@wisc.edu"]},{"name":"Harrison Graham Pope","id":"openalex:A5005998157","orcid":"https://orcid.org/0000-0003-1298-6439","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Harvard University"]},{"name":"Justine K. Lalonde","id":"openalex:A5101769859","orcid":"https://orcid.org/0000-0001-8196-460X","affiliation":"AstraZeneca (United Kingdom)","institutionId":"I105036370","institutionUrl":"https://ror.org/04r9x1a08","affiliations":["AstraZeneca","Harvard University"]},{"name":"Jacqueline L. Roberts","id":"openalex:A5075084595","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Harvard University"]},{"name":"Yael I. Nillni","id":"openalex:A5081973715","orcid":"https://orcid.org/0000-0001-8876-0415","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Harvard University"]},{"name":"Nan M. Laird","id":"openalex:A5107379548","orcid":"https://orcid.org/0000-0003-2066-3234","affiliation":"Harvard University","institutionId":"I136199984","institutionUrl":"https://ror.org/03vek6s52","affiliations":["Harvard University"]},{"name":"Cynthia Marie Bulik","id":"openalex:A5003258692","orcid":"https://orcid.org/0000-0001-7772-3264","affiliation":"University of North Carolina","affiliations":["University of North Carolina"]},{"name":"Scott J. Crow","id":"openalex:A5050826224","orcid":"https://orcid.org/0000-0002-1827-5075","affiliation":"Psychiatry & Behavioral Sciences","affiliations":["Psychiatry & Behavioral Sciences"]},{"name":"Susan L. McElroy","id":"openalex:A5079329164","orcid":"https://orcid.org/0000-0002-8879-0865","affiliation":"University of Cincinnati","institutionId":"I63135867","institutionUrl":"https://ror.org/01e3m7079","affiliations":["University of Cincinnati"]},{"name":"B. Timothy Walsh","id":"openalex:A5011088504","orcid":"https://orcid.org/0000-0001-8626-5859","affiliation":"Columbia University","institutionId":"I78577930","institutionUrl":"https://ror.org/00hj8s172","affiliations":["Columbia University"]}],"venue":"The Journal of Clinical Psychiatry","venueId":"S17992710","publisher":"Physicians Postgraduate Press, Inc.","publisherId":"P4322614534","year":2008,"publishedDate":"2008-02-15","type":"article","citationCount":241,"referenceCount":0,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.4088/jcp.v69n0213","topics":[{"id":"T10263","name":"Eating Disorders and Behaviors","score":1,"subfieldId":"3203","subfield":"Clinical Psychology","fieldId":"32","field":"Psychology"},{"id":"T12873","name":"Nutrition, Health and Food Behavior","score":0,"subfieldId":"2916","subfield":"Nutrition and Dietetics","fieldId":"29","field":"Nursing"},{"id":"T11093","name":"Personality Disorders and Psychopathology","score":0,"subfieldId":"3203","subfield":"Clinical Psychology","fieldId":"32","field":"Psychology"}],"sources":["openalex"],"id":"openalex:W2031508870"},{"ids":{"openalex":"W2053068905","doi":"10.1214/ss/1177010899"},"title":"Regression Models for Discrete Longitudinal Responses","abstract":"$32","authors":[{"name":"Garrett M. 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