Bayesian outcome selection modeling

Author:

Dang Khue‐Dung1ORCID,Ryan Louise M.23,Cook Richard J.4,Akkaya Hocagil Tugba4ORCID,Jacobson Sandra W.5,Jacobson Joseph L.5

Affiliation:

1. School of Mathematics and Statistics University of Melbourne Melbourne 3010 Australia

2. School of Mathematical and Physical Sciences University of Technology Sydney Sydney 2007 Australia

3. Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers Melbourne 3010 Australia

4. Department of Statistics and Actuarial Science University of Waterloo Waterloo N2L 3G1 Canada

5. Department of Psychiatry and Behavioral Neurosciences Wayne State University School of Medicine Detroit Michigan 48201 USA

Abstract

In psychiatric and social epidemiology studies, it is common to measure multiple different outcomes using a comprehensive battery of tests thought to be related to an underlying construct of interest. In the research that motivates our work, researchers wanted to assess the impact of in utero alcohol exposure on child cognition and neuropsychological development, which are evaluated using a range of different psychometric tests. Statistical analysis of the resulting multiple outcomes data can be challenging, because the outcomes measured on the same individual are not independent. Moreover, it is unclear, a priori, which outcomes are impacted by the exposure under study. While researchers will typically have some hypotheses about which outcomes are important, a framework is needed to help identify outcomes that are sensitive to the exposure and to quantify the associated treatment or exposure effects of interest. We propose such a framework using a modification of stochastic search variable selection, a popular Bayesian variable selection model and use it to quantify an overall effect of the exposure on the affected outcomes. The performance of the method is investigated empirically and an illustration is given through application using data from our motivating study.

Funder

Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers

Canadian Institutes of Health Research

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Simultaneous coefficient clustering and sparsity for multivariate mixed models;Journal of Computational and Graphical Statistics;2024-09-13

2. Benchmark dose profiles for bivariate exposures;Risk Analysis;2024-04-23

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