Improving efficiency of inference in clinical trials with external control data
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Suppose we are interested in the effect of a treatment in a clinical trial. The efficiency of inference may be limited due to small sample size of the clinical trial. However, external control data are often available from historical studies. It is appealing to borrow strength from such data to improve efficiency of inference in the clinical trial. Under an exchangeability assumption about the potential outcome mean, we show that the semiparametric efficiency bound for estimating the average treatment effect can be reduced by incorporating both external controls and the clinical trial data. We then derive a doubly robust and locally efficient estimator. We show that the improvement in efficiency is prominent especially when the external control dataset has a large sample size and small variability. Our method allows for a relaxed overlap assumption, and we illustrate with the case where the clinical trial only contains an active treated group. We also develop a doubly robust and locally efficient approach that extrapolates the causal effect in the clinical trial to the overall population. Our results are also useful for trial design and data collection. We evaluate the finite-sample performance of the proposed estimators via simulation. In application to a clinical study comparing the effect of a combination treatment on Helicobacter pylori infection to that of the conventional triple therapy, our approach shows that the combination treatment has efficacy advantages.
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