Exact Bayesian p-values for a test of independence in a 2 × 2 contingency table with missing data

Author:

Lin Yan1,Lipsitz Stuart R2,Sinha Debajyoti3,Fitzmaurice Garrett4,Lipshultz Steven5

Affiliation:

1. Biostatistics Department, MD Anderson Cancer Center, Houston, TX, USA

2. Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA, USA

3. Department of Statistics, The Florida State University, Tallahassee, FL, USA

4. Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, MA, USA

5. Department of Pediatrics, Wayne State University Medical School, Detroit, MI, USA

Abstract

Altham (Altham PME. Exact Bayesian analysis of a 2 × 2 contingency table, and Fisher’s “exact” significance test. J R Stat Soc B 1969; 31: 261–269) showed that a one-sided p-value from Fisher’s exact test of independence in a 2 × 2 contingency table is equal to the posterior probability of negative association in the 2 × 2 contingency table under a Bayesian analysis using an improper prior. We derive an extension of Fisher’s exact test p-value in the presence of missing data, assuming the missing data mechanism is ignorable (i.e., missing at random or completely at random). Further, we propose Bayesian p-values for a test of independence in a 2 × 2 contingency table with missing data using alternative priors; we also present results from a simulation study exploring the Type I error rate and power of the proposed exact test p-values. An example, using data on the association between blood pressure and a cardiac enzyme, is presented to illustrate the methods.

Publisher

SAGE Publications

Subject

Health Information Management,Statistics and Probability,Epidemiology

Cited by 3 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. Multiple imputation for non-monotone missing not at random data using the no self-censoring model;Statistical Methods in Medical Research;2023-08-30

2. Bayesian analysis of survival data with missing censoring indicators;Biometrics;2020-05-04

3. Understanding Measures of Association;American Journal of Physical Medicine & Rehabilitation;2019

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