Inference in response-adaptive clinical trials when the enrolled population varies over time

Massimiliano Russo, Steffen Ventz, Victoria Wang, Lorenzo Trippa

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments, can lead to biased treatment effect estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type-I error rates. The first procedure models trends of patient outcomes with splines. The second leverages conditional inference principles, which have been introduced to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response-adaptive clinical trials. We illustrate the consequences of trends in the outcome distributions in response-adaptive designs and in platform trials, and investigate the proposed methods in the analysis of a glioblastoma study.

Original languageEnglish (US)
Pages (from-to)381-393
Number of pages13
JournalBiometrics
Volume79
Issue number1
DOIs
StatePublished - Mar 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 The International Biometric Society.

Keywords

  • adaptive trial design
  • bootstrap
  • glioblastoma
  • platform trials
  • population trends
  • splines

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural

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