Design and Analysis of Group Sequential Trials for Repeated Measurements When Pipeline Data Occurs: A Tutorial

• Published in: Statistics in medicine Vol. 44; no. 13-14; pp. e70130 - n/a
• Main Authors: Baayen, Corine, Blanche, Paul, Jennison, Christopher, Ozenne, Brice
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2025; Wiley Subscription Services, Inc
• Subjects: Clinical Trials as Topic - methods; Clinical Trials as Topic - statistics & numerical data; Data Interpretation, Statistical; delayed outcomes; Endpoint Determination - methods; error spending functions; group sequential trials; Humans; Models, Statistical; pipeline patients; repeated measurements; Research Design; pipeline patients; delayed outcomes; error spending functions; group sequential trials; repeated measurements
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.70130

ABSTRACT Group sequential trials (GST) allow for early stopping of a clinical trial for efficacy or futility, without compromising its validity. Statistical methodology for GST is well established when the endpoint is observed immediately, but less so for endpoints that are measured with a delay, such as repeatedly measured outcomes for which the primary measurement of interest is taken after several months. The latter can result in pipeline subjects at an interim analysis. These subjects may have early outcome measurements available, but their final endpoint is yet to be observed. Accounting for these early measurements has been shown to increase statistical power. Most importantly, pipeline patients will contribute with additional data after a decision to stop enrollment has been taken at an interim analysis. To make the best use of all available data, these data are ideally incorporated in the final analysis in a formal way. In this tutorial paper, we provide guidance on how to plan a GST with repeated measurements and a delayed endpoint and how to analyze the data resulting from these trials. We discuss existing methods, and also expand on them, for example adding a nonbinding stopping rule for futility and working out the computational details to derive valid p‐values and confidence intervals. We also provide an R package and R code to perform the methods discussed in this paper.