The use of external control data for predictions and futility interim analyses in clinical trials. Issue 2 (9th June 2021)

• Record Type: Journal Article
• Authors: Ventz, Steffen; Comment, Leah; Louv, Bill; Rahman, Rifaquat; Wen, Patrick Y; Alexander, Brian M; Trippa, Lorenzo
• Is Part Of: Neuro-oncology. Volume 24:Issue 2(2022)
• Page Start: 247
• Page End: 256
• Publication Date: 2021-06-09
• Subjects: external control data -- interim futility analysis -- newly diagnosed glioblastoma -- predictions -- study design; Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noab141
• Languages: English
• ISSNs: 1522-8517

Abstract

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.