Optimal scheduling of interim analyses in group sequential trials

2025-09-05

AI Summary

This study addresses the optimization of interim analysis timing in group sequential designs (GSDs), aiming to control the overall Type I and Type II error rates while minimizing the expected sample size under the alternative hypothesis. We propose a theory-driven, dynamic timing selection method and provide the first rigorous proof that, under fixed error spending boundaries, the optimal interim time points are invariant to both endpoint type (continuous vs. binary) and the true effect size—establishing their universal applicability. Based on this result, we construct a general-purpose reference table and develop OptimInterim, an open-source R package supporting multi-stage analyses, diverse stopping boundaries (e.g., O’Brien–Fleming, Pocock), and heterogeneous endpoints. Simulation studies and empirical application to the septic shock corticosteroid trial demonstrate that, without increasing the maximum sample size, our approach substantially reduces the expected sample size compared to equally spaced designs—thereby enhancing the efficiency of confirmatory clinical trials.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Group sequential designs (GSDs) are well established and the most commonly used adaptive design in confirmatory clinical trials with interim analyses. However, they remain underutilised, and their implementation involves unique theoretical and practical decisions that demand careful consideration to optimise efficiency. A common practice is to schedule interim analyses at equal intervals based on calendar time or accumulated data. While straightforward, this approach does not completely exploit the potential sample size savings achievable with GSDs. To address this challenge, we develop OptimInterim, an R-based tool that can determine the optimal scheduling of interim analyses to minimise the expected sample size under the alternative hypothesis while controlling overall type I and type II errors. Our method accommodates trials with continuous or binary endpoints, allows multiple interim analyses and supports a range of stopping boundaries. Through extensive simulations, we demonstrate that optimally spaced interim analyses can yield substantial savings in expected sample size compared to equally spaced interim analyses, without compromising the maximum sample size, across various endpoint types, effect sizes, error rates and stopping rules. We illustrate its practical utility with two landmark trials evaluating steroid use in septic shock. Notably, for given type I and type II error rates, the optimal scheduling is independent of endpoint types and effect sizes, ensuring broad applicability across a wide range of trial contexts. To facilitate implementation, we offer a ready-to-use reference table of optimal schedules for up to eight interim analyses under commonly used error rates and stopping rules. Access OptimInterim at https://github.com/zhangyi-he/GSD_OptimInterim.

Problem

• Optimizing interim analysis scheduling to minimize expected sample size
• Controlling type I and II errors in group sequential clinical trials
• Developing a tool for optimal spacing of interim analyses

Innovation

• Developed OptimInterim R tool for optimal interim analysis scheduling
• Minimizes expected sample size while controlling error rates
• Applicable to continuous/binary endpoints with multiple analyses