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.

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