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.
Lee
E W
EW
Epidemiology and Biostatistics Program, Institute of Environmental Medicine, New York University Medical Center, New York 10010, USA.
Kim
M Y
MY
D017088:AIDS-Related Opportunistic Infections / Q000097:blood / Q000188:drug therapy; D000704:Analysis of Variance; D000998:Antiviral Agents / Q000009:adverse effects; D001699:Biometry / Q000379:methods*; D017726:Cytomegalovirus Retinitis / Q000097:blood / Q000150:complications / Q000188:drug therapy; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data
D000998:Antiviral Agents