Bias of Odds Ratio Estimate in Fisher's Exact Test

OBJECTIVES: The odds ratio estimate in Fisher's exact test can overestimate the parameter. A simple computer simulation can easily reveal the positive bias of the odds ratio estimate from Fisher's exact test. Bootstrap can facilitate bias correction for the odds ratio estimate. METHODS: The bias can be estimated, using bootstrap samples and the original sample to approximate the expectation of the odds ratio estimator and the true parameter value-their difference is the bias. Here, the bias is computed from the underlying distribution, conditional on the exclusion of zero cells in sampling, to avoid the infinite expectation. RESULTS: A study of depression is used to demonstrate how to use bootstrap to correct the bias in an odds ratio estimate based on Fisher's exact test. CONCLUSIONS: Bootstrapping can easily estimate and correct the bias of an odds ratio estimate in Fisher's exact test. The results suggest that bootstrapping is sensitive enough to detect even a small bias.