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Robust Bayesian Hierarchical Model Using Monte-Carlo Simulation

  • Geng ChenEmail author
  • Sheng Luo
Chapter
Part of the ICSA Book Series in Statistics book series (ICSABSS)

Abstract

Many clinical trials collect information on multiple longitudinal outcomes. Depending on the nature of the disease and its symptoms, the longitudinal outcomes can be of mixed types, e.g., binary, ordinal and continuous. Clinical studies on Parkinson’s disease (PD) are good examples of this case. Due to the multidimensional nature of PD, it is difficult to identify a single outcome to represent the overall disease status and severity. Thus, clinical studies that search for treatments for PD usually collect multiple outcomes at different visits. In this chapter, we will introduce the multilevel item response theory (MLIRT) models that account for all the information from multiple longitudinal outcomes and provide valid inference for the overall treatment effects. We will also introduce the normal/independent (NI) distributions, which can be easily implemented into the MLIRT model hierarchically, to handle the outlier and heavy tails problems to produce robust inference. Other data features such as dependent censoring and skewness will also be discussed under the MLIRT framework.

Keywords

Clinical trial Item-response theory Latent variable MCMC Outliers Joint model Robust distribution Multivariate longitudinal data 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Clinical Statistics, GlaxoSmithKlineCollegevilleUSA
  2. 2.Department of BiostatisticsThe University of Texas Health Science Center at HoustonHoustonUSA

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