Power and Sample Size Calculations in Clinical Trials with Patient-Reported Outcomes under Equal and Unequal Group Sizes Based on Graded Response Model: A Simulation Study

Abstract

ObjectivesTo provide a valid sample size strategy based on simulation and to evaluate the statistical power in clinical trials with patient-reported outcomes (PROs) based on a polytomous item response theory model—the graded response model (GRM)—and to compare this framework with the classical test theory (CTT) approach. MethodsOne thousand randomized clinical trials were simulated using PRO based on the GRM and under various combinations of the number of patients in each arm, the group allocation ratio, the number of items and categories, and group effects. The power and sample size estimated in the simulations were then compared with those computed using the CTT framework. ResultsThe results indicated that the impact of the most influential factors, including the number of patients, group allocation ratio, group effects, and the number of categories, on the power and sample size of the GRM-based and CTT-based approaches was similar. Nevertheless, the strong impact of the number of items on these issues distinguished the two approaches. ConclusionsIt is crucial to use an adapted sample size formula in a GRM-based analysis because the classical formula designed for the CTT-based approach does not consider the impact of the number of items, which could result in an inadequately sized study and a decrease in power. Thus, when clinicians design a randomized clinical trial with polytomous PRO endpoints using classical sample size formula as the base, they should be aware of the possibility of making an incorrect clinical decision.