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1.  Using a Bayesian latent growth curve model to identify trajectories of positive affect and negative events following myocardial infarction 
Biostatistics (Oxford, England)  2005;6(1):119-143.
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.
PMCID: PMC2827342  PMID: 15618532
Cardiovascular disease; Depression; DIC; General growth mixture modeling; Gibbs sampling; Label switching; Model choice
2.  Longitudinal profiling of health care units based on continuous and discrete patient outcomes 
Monitoring health care quality involves combining continuous and discrete outcomes measured on subjects across health care units over time. This article describes a Bayesian approach to jointly modeling multilevel multidimensional continuous and discrete outcomes with serial dependence. The overall goal is to characterize trajectories of traits of each unit. Underlying normal regression models for each outcome are used and dependence among different outcomes is induced through latent variables. Serial dependence is accommodated through modeling the pairwise correlations of the latent variables. Methods are illustrated to assess trends in quality of health care units using continuous and discrete outcomes from a sample of adult veterans discharged from 1 of 22 Veterans Integrated Service Networks with a psychiatric diagnosis between 1993 and 1998.
PMCID: PMC2791405  PMID: 15917373
Bayesian hierarchical model; Correlation matrix; Informative priors; Latent variable; Mental health
3.  Accuracy of MSI testing in predicting germline mutations of MSH2 and MLH1: a case study in Bayesian meta-analysis of diagnostic tests without a gold standard 
Biostatistics (Oxford, England)  2005;6(3):450-464.
Microsatellite instability (MSI) testing is a common screening procedure used to identify families that may harbor mutations of a mismatch repair (MMR) gene and therefore may be at high risk for hereditary colorectal cancer. A reliable estimate of sensitivity and specificity of MSI for detecting germline mutations of MMR genes is critical in genetic counseling and colorectal cancer prevention. Several studies published results of both MSI and mutation analysis on the same subjects. In this article we perform a meta-analysis of these studies and obtain estimates that can be directly used in counseling and screening. In particular, we estimate the sensitivity of MSI for detecting mutations of MSH2 and MLH1 to be 0.81 (0.73–0.89). Statistically, challenges arise from the following: (a) traditional mutation analysis methods used in these studies cannot be considered a gold standard for the identification of mutations; (b) studies are heterogeneous in both the design and the populations considered; and (c) studies may include different patterns of missing data resulting from partial testing of the populations sampled. We address these challenges in the context of a Bayesian meta-analytic implementation of the Hui–Walter design, tailored to account for various forms of incomplete data. Posterior inference is handled via a Gibbs sampler.
PMCID: PMC2274000  PMID: 15831578
Diagnostic test; Hereditary nonpolyposis colorectal cancer; Microsatellite instability; Sensitivity; Specificity

Results 1-3 (3)