Justifying the sample size for a factorial trial

Music City Center 

Factorial trials continue to grow in popularity as a method to test multiple combinations of intervention components simultaneously in a single randomized trial, but there is still a lack of clarity around how to determine a sufficient sample size. Part of this confusion stems from the fact that study teams conduct factorial trials for different reasons. In this talk, we will consider three research questions that could motivate a factorial trial: 1) identifying intervention components that have a statistically significant effect on the outcome; 2) identifying statistically significant interactions between intervention components; 3) determining which combination of components is most likely to have an optimal effect on the outcome. For each of these potential research questions, we discuss how to approach a sample size justification/power analysis. We show that studies that are powered to address research question 1 are sufficient to reach decisions within 10% of the optimal combination in answer to research question 3, but that addressing research question 2 can require considerably larger sample sizes. We introduce an R shiny package that assists with these calculations.

factorial trial

power analysis 

Biopharmaceutical Section