Testing for Treatment Effect Twice Using Internal and External Controls in Clinical Trials
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Leveraging external controls – relevant individual patient data under control from external trials or real-world data – has the potential to reduce the cost of randomized controlled trials (RCTs) while increasing the proportion of trial patients given access to novel treatments. However, due to lack of randomization, RCT patients and external controls may differ with respect to covariates that may or may not have been measured. Hence, after controlling for measured covariates, for instance by matching, testing for treatment effect using external controls may still be subject to unmeasured biases. In this paper, we propose a sensitivity analysis approach to quantify the magnitude of bias from unmeasured covariates that would need to be present to alter the study conclusion that presumed no unmeasured biases are introduced by employing external controls. Whether leveraging external controls increases power or not depends on the interplay between sample sizes and the magnitude of treatment effect and unmeasured biases, which may be difficult to anticipate. This motivates a combined testing procedure that performs two highly correlated analyses, one with and one without external controls, correcting for multiple testing using the joint distribution of the two test statistics. In theory, power calculations, and a real application, it is shown that the gains from using the combined testing procedure are often large and the losses due to correction for multiple testing are often small.
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