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

Medicine and Health

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

S. Ventz, S. Khozin, et al.

This innovative research, conducted by a talented team including Steffen Ventz, Sean Khozin, and others, unveils a hybrid clinical trial design that melds external control datasets with randomization, optimizing treatment effect inference and addressing confounders in patient characteristics. Through simulations involving ES-SCLC and GBM studies, the study showcases the superiority of this approach over traditional trial methodologies.

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~3 min • Beginner • English
Abstract
Patient-level data from completed clinical studies or electronic health records can be used in the design and analysis of clinical trials. However, these external data can bias the evaluation of the experimental treatment when the statistical design does not appropriately account for potential confounders. In this work, we introduce a hybrid clinical trial design that combines the use of external control datasets and randomization to experimental and control arms, with the aim of producing efficient inference on the experimental treatment effects. Our analysis of the hybrid trial design includes scenarios where the distributions of measured and unmeasured prognostic patient characteristics differ across studies. Using simulations and datasets from clinical studies in extensive-stage small cell lung cancer and glioblastoma, we illustrate the potential advantages of hybrid trial designs compared to externally controlled trials and randomized trial designs.
Publisher
Nature Communications
Published On
Oct 02, 2022
Authors
Steffen Ventz, Sean Khozin, Bill Louv, Jacob Sands, Patrick Y. Wen, Rifaquat Rahman, Leah Comment, Brian M. Alexander, Lorenzo Trippa
Tags
hybrid clinical trial design
external control datasets
treatment effect inference
confounders
ES-SCLC
GBM
clinical research
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