Comparison of different maximum likelihood estimators in a small sample logistic regression with two independent binary variables

摘要

AbstractIn order to examine the bias of the estimate of the log odds ratio in a 2 × 2 contingency table, Walter computed the entire distribution of the estimated log odds ratio using various small sample sizes. This is equivalent to computing the distribution of the estimated parameterb1in a logistic regression with one independent binary variable. In this paper, the distributions of the estimated parametersb1andb2for two independent binary variables are computed for some small sample logistic regressions using six different estimation methods based on maximum likelihood. These estimates are then compared to the true parameter values. The best estimation method depends on the frequency of the outcome of interest and on whether the bias or mean square error is considered more important