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1.  A General Class of Pattern Mixture Models for Nonignorable Dropout with Many Possible Dropout Times 
Biometrics  2007;64(2):538-545.
Summary
In this article we consider the problem of fitting pattern mixture models to longitudinal data when there are many unique dropout times. We propose a marginally specified latent class pattern mixture model. The marginal mean is assumed to follow a generalized linear model, whereas the mean conditional on the latent class and random effects is specified separately. Because the dimension of the parameter vector of interest (the marginal regression coefficients) does not depend on the assumed number of latent classes, we propose to treat the number of latent classes as a random variable. We specify a prior distribution for the number of classes, and calculate (approximate) posterior model probabilities. In order to avoid the complications with implementing a fully Bayesian model, we propose a simple approximation to these posterior probabilities. The ideas are illustrated using data from a longitudinal study of depression in HIV-infected women.
doi:10.1111/j.1541-0420.2007.00884.x
PMCID: PMC2791415  PMID: 17900312
Bayesian model averaging; Incomplete data; Latent variable; Marginal model; Random effects
2.  A Class of Markov Models for Longitudinal Ordinal Data 
Biometrics  2007;63(4):1060-1067.
Summary
Generalized linear models with serial dependence are often used for short longitudinal series. Heagerty (2002, Biometrics 58, 342–351) has proposed marginalized transition models for the analysis of longitudinal binary data. In this article, we extend this work to accommodate longitudinal ordinal data. Fisher-scoring algorithms are developed for estimation. Methods are illustrated on quality-of-life data from a recent colorectal cancer clinical trial.
doi:10.1111/j.1541-0420.2007.00800.x
PMCID: PMC2766273  PMID: 18078479
Fisher scoring; Generalized linear models; QOL

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