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Biostatistics 2005 6(1):119-143; doi:10.1093/biostatistics/kxh022
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Biostatistics Vol. 6 No. 1 © Oxford University Press 2005; all rights reserved.

Using a Bayesian latent growth curve model to identify trajectories of positive affect and negative events following myocardial infarction

Michael R. Elliott*

Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, 612 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA melliott{at}cceb.upenn.edu

Joseph J. Gallo

Department of Family Practice and Community Medicine, University of Pennsylvania School of Medicine, USA

Thomas R. Ten Have

Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, USA

Hillary R. Bogner

Department of Family Practice and Community Medicine, University of Pennsylvania School of Medicine, USA

Ira R. Katz

Department of Psychiatry, University of Pennsylvania School of Medicine, USA

* To whom correspondence should be addressed.

Positive and negative affect data are often collected over time in psychiatric care settings, yet no generally accepted means are available to relate these data to useful diagnoses or treatments. Latent class analysis attempts data reduction by classifying subjects into one of K unobserved classes based on observed data. Latent class models have recently been extended to accommodate longitudinally observed data. We extend these approaches in a Bayesian framework to accommodate trajectories of both continuous and discrete data. We consider whether latent class models might be used to distinguish patients on the basis of trajectories of observed affect scores, reported events, and presence or absence of clinical depression.

Keywords: Cardiovascular disease; Depression; DIC; General growth mixture modeling; Gibbs sampling; Label switching; Model choice


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