Group sequential designs and a sample size reestimation approach for partially nested trials

Abstract

Abstract Background Randomized controlled trials are the gold standard for evaluating interventions. For individually randomized trials, clustering can exist in the intervention arm but not the control arm when the intervention is delivered by study personnels and/or in a group setting. This results in a partially nested design, which methodological investigations have so far been limited to fixed trial settings. We examine whether existing frameworks for group sequential design and sample size reestimation (SSR) based on estimated variance parameters can be directly applied to partially nested trials, where a heteroscedastic mixed effect model is implemented. Methods We propose computing stopping boundaries in the standard way and inflating the required sample size by the group sequential design factor. We consider two recruitment strategies that lead to the same amount of data at the interim analysis of a partially nested design. For SSR, we assume half the initially planned number of clusters are enrolled at the start of the trial. We propose updating the required number of clusters — given the same cluster size as initially planned — using variance estimates obtained at the interim analysis. We conduct proof-of-concept simulation studies to evaluate the operating characteristics of the two types of adaptive designs, respectively. Results Most of the computed group sequential partially nested designs meet power requirement under both recruitment strategies considered. However, enrolling all clusters at the start — resulting in only data from half the cluster size being available at the interim analysis — produces a greater likelihood of stopping early for efficacy compared with recruiting only half the number of clusters initially (which provides data of full cluster size at interim). For SSR, the updated number of clusters is on average close to the required number for achieving the target power, although its median is slightly lower. Both designs lead to Type I error rates that are close to the nominal values in most cases. Conclusions With careful planning informed by clinical trial simulations, the proposed group sequential and SSR partially nested designs can satisfy the error-rate control requirements, noting that conventional estimation methods may produce biased treatment effect estimates and confidence intervals.