Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Data
arxiv(2024)
Abstract
We consider the problem of estimating the average treatment effect (ATE) when
both randomized control trial (RCT) data and real-world data (RWD) are
available. We decompose the ATE estimand as the difference between a pooled-ATE
estimand that integrates RCT and RWD and a bias estimand that captures the
conditional effect of RCT enrollment on the outcome. We introduce an adaptive
targeted minimum loss-based estimation (A-TMLE) framework to estimate them. We
prove that the A-TMLE estimator is root-n-consistent and asymptotically normal.
Moreover, in finite sample, it achieves the super-efficiency one would obtain
had one known the oracle model for the conditional effect of the RCT enrollment
on the outcome. Consequently, the smaller the working model of the bias induced
by the RWD is, the greater our estimator's efficiency, while our estimator will
always be at least as efficient as an efficient estimator that uses the RCT
data only. A-TMLE outperforms existing methods in simulations by having smaller
mean-squared-error and 95
to improve the efficiency of randomized trial results without biasing the
estimates of intervention effects. This approach could allow for smaller,
faster trials, decreasing the time until patients can receive effective
treatments.
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