In epidemiologic studies, attempts at the assessment of effects of interest with possible risk factors, with suitable adjustments for confounding variables, are often made. The research provides one effective way of dealing with this problem that has well characterized properties even in small samples. The procedures are demonstrated to be better than others currently available, and can be applied directly in analyzing existing Environmental Protection Agency (EPA) epidemiologic data. Small sample properties of old and new odds ratio estimators are studied with regard to bias and mean squared error using Monte Carlo experiments. Their performances in interval estimation are also compared. A new and simple estimator is found to perform better than almost any other estimator, including the conditional likelihood estimator, both for point and interval estimation for a wide range of sample sizes.