Meta analysis of binary data with excessive zeros in two-arm trials

Saman Muthukumarana (), David Martell () and Ram Tiwari ()
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Saman Muthukumarana: Department of Statistics, University of Manitoba
David Martell: ITAM
Ram Tiwari: Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration

Journal of Statistical Distributions and Applications, 2019, vol. 6, issue 1, 1-17

Abstract: Abstract We present a novel Bayesian approach to random effects meta analysis of binary data with excessive zeros in two-arm trials. We discuss the development of likelihood accounting for excessive zeros, the prior, and the posterior distributions of parameters of interest. Dirichlet process prior is used to account for the heterogeneity among studies. A zero inflated binomial model with excessive zero parameters were used to account for excessive zeros in treatment and control arms. We then define a modified unconditional odds ratio accounting for excessive zeros in two arms. The Bayesian inference is carried out using Markov chain Monte Carlo (MCMC) sampling techniques. We illustrate the approach using data available in published literature on myocardial infarction and death from cardiovascular causes. Bayesian approaches presented here use all the data, including the studies with zero events and capture heterogeneity among study effects, and produce interpretable estimates of overall and study-level odds-ratios, over the commonly used frequentist’s approaches. Results from the data analysis and the model selection also indicate that the proposed Bayesian method, while accounting for zero events, adjusts for excessive zeros and provides better fit to the data resulting in the estimates of overall odds-ratio and study-level odds-ratios that are based on the totality of the information.

Keywords: Dirichlet process; Model selection; Markov chain Monte Carlo; Simulation (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1186/s40488-019-0099-x

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