Modeling The Relationship Between Progression Of CD4-Lymphocyte Count And Survival Time
In models for repeated observations of a measured response, the length of the response vector may be determined by a survival process related to the response. If the measurement error is large, and probability of death depends on the true, unobserved value of the response, then the survival process must be modelled. Wu and Carroll (1988) proposed a random effects model for a two-sample longitudinal data in the presence of informative censoring, in which the individual effects included only slopes and intercepts. We propose methods for fitting a broad class of models of this type, in which both the repeated measures and the survival time are modelled using random effects. These methods permit us to estimate parameters describing the relationship between measures of disease progression and survival time; and we apply them to results of AIDS clinical trials.
KeywordsSurvival Time American Statistical Association Growth Curve Model Middle Curve Informative Censoring
Unable to display preview. Download preview PDF.
- DeGruttola, V., Wulfsohn, M. and Tsiatis, A. (1990) “Modeling the relationship between survival after AIDS diagnosis and progression of markers of HIV disease,” Technical Report, Harvard School of Public Health.Google Scholar
- Dempster, A.P., Rubin, D.B. and Laird, N.M. (1977) “Maximum likelihood with incomplete data via the E-M algorithm,” Journal of the Royal Statistical Society, Series B 39, 1–38.Google Scholar
- Meng and Rubin (1990) “Using EM to obtain asymptotic variance-covar iance matrices: the SEM algorithm,” Journal of the American Statistical Association, to appear.Google Scholar
- Rubin, D.B. (1987b), Comment on “The calculation of posterior distributions by data augmentation,” by M.A. Tanner and W.H. Wong, Journal of the American Statistical Association, 82, 543–546.Google Scholar
- Turnbull (1976), “The empirical distribution function with arbitrarily grouped, censored, and truncated data,” Journal of the Royal Statistical Society, Series B, 38, 290–295.Google Scholar