The use of external control data for predictions and futility interim analyses in clinical trials

Steffen Ventz, Leah Comment, Bill Louv, Rifaquat Rahman, Patrick Y. Wen, Brian M. Alexander, Lorenzo Trippa

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Background: External control (EC) data from completed clinical trials and electronic health records can be valuable for the design and analysis of future clinical trials. We discuss the use of EC data for early stopping decisions in randomized clinical trials (RCTs). Methods: We specify interim analyses (IAs) approaches for RCTs, which allow investigators to integrate external data into early futility stopping decisions. IAs utilize predictions based on early data from the RCT, possibly combined with external data. These predictions at IAs express the probability that the trial will generate significant evidence of positive treatment effects. The trial is discontinued if this predictive probability becomes smaller than a prespecified threshold. We quantify efficiency gains and risks associated with the integration of external data into interim decisions. We then analyze a collection of glioblastoma (GBM) data sets, to investigate if the balance of efficiency gains and risks justify the integration of external data into the IAs of future GBM RCTs. Results: Our analyses illustrate the importance of accounting for potential differences between the distributions of prognostic variables in the RCT and in the external data to effectively leverage external data for interim decisions. Using GBM data sets, we estimate that the integration of external data increases the probability of early stopping of ineffective experimental treatments by up to 25% compared to IAs that do not leverage external data. Additionally, we observe a reduction of the probability of early discontinuation for effective experimental treatments, which improves the RCT power. Conclusion: Leveraging external data for IAs in RCTs can support early stopping decisions and reduce the number of enrolled patients when the experimental treatment is ineffective.

Original languageEnglish (US)
Pages (from-to)247-256
Number of pages10
JournalNeuro-Oncology
Volume24
Issue number2
DOIs
StatePublished - Feb 1 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 The Author(s). Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved.

Keywords

  • external control data
  • interim futility analysis
  • newly diagnosed glioblastoma
  • predictions
  • study design

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