MODEL-BASED APPROACHES TO ANALYSING INCOMPLETE LONGITUDINAL AND FAILURE TIME DATA
Joseph W. Hogana b, Nan M. Lairdc d
- aCenter for Statistical Sciences, Brown University, Providence, RI 02192, USA
- bCenter for Statistical Sciences, Brown University, Box G-H, Providence, RI 02192, U.S.A
- cDepartment of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue Boston, MA 02115, U.S.A
- dDepartment of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115 U.S.A
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
Abstract
Since Wu and Carroll (Biometrics 44, 175-188) proposed a model for longitudinal progression in the presence of informative dropout, several researchers have developed and studied models for situations where both a vector of repeated outcomes and an event time is available for each subject. 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.
TL;DRSeveral models which are used to handle incomplete response and covariate data in both survival and longitudinal studies are reviewed. (Semantic Scholar)