 Logistic regression models are commonly used to estimate the probability of an event given certain covariates. However, when there are few observations available at both the outcome and covariate level, the models may produce biased estimates due to data sparseness. In this paper, we compared four different approaches to reduce the bias caused by sparse data, including the maximum likelihood, ML, FIRTS, EXACT, and Bayesian methods. Our simulation study showed that the Bayesian methods with hyper-gamma prior modeling of the prior covariance matrix for regression coefficients were more effective than the other three methods in reducing the bias under the null hypothesis. Additionally, the Bayesian methods with log F-type priors were found to be better than the other two methods in reducing the bias under the alternative hypothesis. These findings suggest that the Bayesian methods with hyper-gamma prior and log F-type priors should be preferred over the other three methods when fitting logistic models to sparse datasets. This article was authored by Masahiko Gashu, Tomohiro Ohigashi, Kengo Nagashima, and others. We are article.tv, links in the description below.