This work introduces a hybrid clinical trial design combining external control datasets and randomization to improve the efficiency of treatment effect inference. The design accounts for potential confounders arising from differences in patient characteristics across studies. Simulations and datasets from extensive-stage small cell lung cancer (ES-SCLC) and glioblastoma (GBM) studies illustrate the advantages of this hybrid approach compared to externally controlled trials (ECTs) and randomized controlled trials (RCTs).
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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