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Bayesian Hierarchical Joint Modeling Using Skew-Normal/Independent Distributions.

ABSTRACT: The multiple longitudinal outcomes collected in many clinical trials are often analyzed by multilevel item response theory (MLIRT) models. The normality assumption for the continuous outcomes in the MLIRT models can be violated due to skewness and/or outliers. Moreover, patients' follow-up may be stopped by some terminal events (e.g., death or dropout) which are dependent on the multiple longitudinal outcomes. We proposed a joint modeling framework based on the MLIRT model to account for three data features: skewness, outliers, and dependent censoring. Our method development was motivated by a clinical study for Parkinson's disease.

SUBMITTER: Chen G 

PROVIDER: S-EPMC6114938 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Bayesian Hierarchical Joint Modeling Using Skew-Normal/Independent Distributions.

Chen Geng G   Luo Sheng S  

Communications in statistics: Simulation and computation 20170628 5

The multiple longitudinal outcomes collected in many clinical trials are often analyzed by multilevel item response theory (MLIRT) models. The normality assumption for the continuous outcomes in the MLIRT models can be violated due to skewness and/or outliers. Moreover, patients' follow-up may be stopped by some terminal events (e.g., death or dropout) which are dependent on the multiple longitudinal outcomes. We proposed a joint modeling framework based on the MLIRT model to account for three d  ...[more]

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