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Timing of the interim analysis in adaptive enrichment designs. | LitMetric

Timing of the interim analysis in adaptive enrichment designs.

J Biopharm Stat

a Department of Medical Biometry , Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg , Germany.

Published: October 2019

AI Article Synopsis

  • With the rise of personalized medicine, research designs that can show treatment efficacy in specific patient subgroups are becoming increasingly important.
  • Adaptive enrichment designs allow for the selection of these subgroups through an interim analysis, but the timing of this analysis has not been extensively studied.
  • This research examines how the timing of the interim analysis affects study power based on different selection rules, revealing that while timing can significantly influence results, there is no one-size-fits-all solution; it must be tailored to the specific research scenario.

Article Abstract

With increasing interest in personalized medicine over the last years, study designs allowing to demonstrate efficacy in particular subgroups of the overall patient population become more important. Adaptive enrichment designs provide the possibility to both selecting the target population with the most promising treatment benefit and testing for efficacy within a single trial. Here, the target population is selected in a prespecified interim analysis. So far, it has not been very well investigated how timing of the interim analysis should be chosen. We investigate the impact of the interim analysis timing on power for the situation of a normally distributed outcome considering two different classes of selection rules. The interim selection is based either on the estimated effect difference between subgroup and total population or on absolute effect estimates. In this article, we demonstrate that there are indeed scenarios in which the timing of the interim analysis has a large impact on power. However, no universally applicable timing with favorable performance exist since power depends on treatment effects, subgroup prevalence, and especially the applied selection rule. Instead, the operating characteristics should be investigated for the specific scenario at hand to determine the most appropriate timing.

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Source
http://dx.doi.org/10.1080/10543406.2017.1372769DOI Listing

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