Statistical evaluation of absolute change versus responder analysis in clinical trials

Abstract

In clinical trials, the primary analysis is often either a test of absolute/relative change in a measured outcome or a corresponding responder analysis. Although each of these tests may be reasonable, determining which test is most suitable for a particular research study remains an open question. These tests may require different sample sizes or define different clinically meaningful differences; most importantly, they may lead to different study conclusions. The aim of this study was to compare a typical non-inferiority test using absolute change as the study endpoint to the corresponding responder analysis in terms of sample-size requirements, statistical power, and hypothesis-testing results. From numerical analysis, using absolute change as an endpoint generally requires a larger sample size; therefore, when the sample size is the same, the responder analysis has higher power. The cut-off value and non-inferiority margin are critical and can meaningfully affect whether the two types of endpoints yield conflicting conclusions. Specifically, extreme cut-off values are more likely to yield different conclusions. However, this influence decreases as population variance increases. One important reason for conflicting conclusions is a non-normal population distribution. To eliminate conflicting results, researchers should consider the population distribution and cut-off value selection.