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ABSTRACT:
In biomedical research with longitudinal designs, missing values due to intermittent nonresponse or premature withdrawal are usually ’nonignorable’ in the sense that un- observed values are related to the patterns of missingness. When missing values are simply ignored, analyses based on observed-data likelihood may yield biased estimates or invalid inferences. By drawing the framework of a shared-parameter mechanism, the process yielding the repeated count measures and the process yielding missing val- ues can be modelled separately, conditionally on a group of shared parameters. For chronic diseases, Markov transition models can be used to study the transitional fea- tures of the pathologic processes. In this paper, Markov chain Monte Carlo (MCMC) algorithms are developed to fit a random-effects Markov transition model (REMTM) for incomplete count repeated measures, within which random effects are shared by the counting process and the missing-data mechanism. Assuming a Poisson distribu- tion for the count measures, the transition probabilities are estimated using a Poisson linear regression model. The missingness mechanism is modeled with a multinomial- logit regression to calculate the transition probabilities of the missingness indicators. The method is demonstrated using both simulated data sets and a practical data set from a smoking cessation clinical trial

SUGGESTED CITATION:
Jinhui Li, Xiaowei Yang, Ying N. Wu, and Steven Shoptaw, "A Random-effects Markov Transition Model for Poisson-distributed Repeated Measures with Nonignorable Missing Values" (October 28, 2005). Department of Statistics, UCLA. Department of Statistics Papers. Paper 2005102802.
http://repositories.cdlib.org/uclastat/papers/2005102802