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Bias of Odds Ratio Estimate in Fisher's Exact Test
1University of South Carolina, Columbia, South Carolina, USA.
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.
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