The analysis of correlated panel data using a continuous-time Markov model

We consider the analysis of correlated panel data in which two or more correlated multistate processes are periodically observed on each individual and the exact transition times between states are unknown. We describe a procedure that models each process marginally under a time-homogeneous Markov model allowing for covariates. The resulting estimators are shown to be asymptotically jointly normal with a covariance matrix that can be consistently estimated. Simultaneous inference procedures are also proposed. Methods are illustrated using data from an AIDS clinical trial to compare the toxic effects of two treatments on two hematologic variables, hemoglobin and absolute neutrophil count.