Subject-specific and Population-averaged Continuation Ratio Logit Models for Multiple Discrete Time Survival Profiles

Subject-specific and population-averaged continuation ratio logit models are presented for multivariate discrete time survival data. The models characterize data from a psychological experiment by using a quadratic polynomial relationship across time that depends on a time-independent condition. A multivariate normal random effects distribution is imposed on intercept, linear and quadratic terms in the subject-specific model, which is fitted by using a combination of Gibbs sampling and buffered stochastic substitution. Variance components that tend towards 0 are addressed in this context. In addition, generalized estimating equations estimates of the parameters in the population-averaged model are compared with analogous estimates for the mixed effects model.