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

Abstract

Purpose External control 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 external control 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 pre-specified 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) datasets, 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 datasets, 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 don’t 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.

The design and evaluation of hybrid controlled trials that leverage external data and randomization

“…The increasing availability of patient-level data from completed clinical studies and electronic health records constitutes an opportunity for the development of novel trial designs that leverage EC data 5 , 7 , 9 , 15 , 18 . Recent contributions 5 , 8 , 18 have proposed methodologies to integrate EC data into the analysis of single-arm trials (ECTs). These methods replace published estimates of the SOC’s efficacy used as a benchmark with patient-level EC data.…”

“…The integration of EC data in the design and analysis of clinical trials can take several forms, including testing/estimating treatment effects upon study completion 5 , sample size re-estimation at interim analyses (IAs), and early decisions to terminate the study for futility or efficacy 7 , 8 . With the increasing availability of data from past trials, the prospective use of EC data in the design, conduct, and analysis of clinical trials has the potential to reduce the cost and time of evaluating new treatments 6 , 9 , 10 .…”

The Design and Evaluation of Hybrid Controlled Trials that Leverage External Data and Randomization

“…The increasing availability of patient-level data from completed clinical studies and electronic health records constitutes an opportunity for the development of novel trial designs that leverage EC data 7,9,15,17,19 . Recent contributions 8,17,19 have proposed methodologies to integrate EC data into the analysis of single-arm trials (ECTs). These methods replace published estimates of the SOC's e cacy used as a benchmark with patient-level EC data.…”

“…The analyses based on model-based simulations (Table 2) and in silico trials obtained by resampling the GBM datasets 8 (Table 3) indicated potential e ciency gains of HTs compared to RCTs when EC data without substantial limitations are available. We showed improvements of power, average study duration, and sample size.…”

“…The integration of EC data in the design and analysis of clinical trials can take several forms, including testing/estimating treatment effects upon study completion 5 , sample size re-estimation at interim analyses (IAs), and early decisions to terminate the study for futility or e cacy 7,8 . With the increasing availability of data from past trials, the prospective use of EC data in the design, conduct, and analysis of clinical trials has the potential to reduce the cost and time of evaluating new treatments 6,9,10 .…”