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Robust Bayesian Hierarchical Model Using Monte-Carlo Simulation

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Book cover Monte-Carlo Simulation-Based Statistical Modeling

Part of the book series: ICSA Book Series in Statistics ((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.

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Authors and Affiliations

  1. Clinical Statistics, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, PA, 19426, USA

    Geng Chen

  2. Department of Biostatistics, The University of Texas Health Science Center at Houston, 1200 Pressler St., Houston, TX, USA

    Sheng Luo

Authors
  1. Geng Chen
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  2. Sheng Luo
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Correspondence to Geng Chen .

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Editors and Affiliations

  1. Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA

    Ding-Geng (Din) Chen

  2. Risk Management, Credit Suisse Risk Management, New York, New York, USA

    John Dean Chen

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Chen, G., Luo, S. (2017). Robust Bayesian Hierarchical Model Using Monte-Carlo Simulation. In: Chen, DG., Chen, J. (eds) Monte-Carlo Simulation-Based Statistical Modeling . ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-3307-0_16

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