Sample Size Formulas for Estimating Risk Ratios with the Modified Poisson Model for Binary Outcomes
Abstract
Sample size estimation is usually the first step in planning a research study. Too small a study cannot adequately address the objectives, while too large a study may waste resources or unethical. For binary outcomes, several sample size estimation methods are available based on logistic regression models, which focusing on odds ratios. In prospective studies, risk ratios are preferable for ease of interpretation and communication. In this thesis, we compared the power difference between the logistic regression model and the modified Poisson regression model via simulation studies. We then proposed sample size estimation formulas based on the modified Poisson regression model for estimating risk ratios. Simulation results suggested that both models have similar performance in terms of Type I error and power. The empirical evaluation indicated that the proposed sample size formulas are reliable in a wide range of scenarios. The sample size estimation procedure was illustrated using a subset of data from the Diabetes Control and Complications Trial.