Comparison of Dichotomized and Distributional Approaches in Rare Event Clinical Trial Design: a Fixed Bayesian Design

This research was motivated by our goal to design an efficient clinical trial to compare two doses of docosahexaenoic acid supplementation for reducing the rate of earliest preterm births and/or preterm births. Dichotomizing continuous gestational age data using a classic binomial distribution will result in a loss of information and reduced power. A distributional approach is an improved strategy to retain statistical power from the continuous distribution. However, appropriate distributions that fit the data properly, particularly in the tails, must be chosen, especially when the data are skewed. A recent study proposed a skew-normal method. We propose a three-component normal mixture model and introduce separate treatment effects at different components of gestational age. We evaluate operating characteristics of mixture model, beta-binomial model, and skew-normal model through simulation. We also apply these three methods to data from two completed clinical trials from the USA and Australia. Finite mixture models are shown to have favorable properties in preterm births analysis but minimal benefit for earliest preterm births analysis. Normal models on log transformed data have the largest bias. Therefore we recommend finite mixture model for preterm births study. Either finite mixture model or beta-binomial model is acceptable for earliest preterm births study.