Using synergies between survey statistics and causal inference to improve transportability of clinical trials
Randomised trials have been the gold standard for assessing causal effects since its introduction by Fisher in the 1920s, since they eliminate both observed and unobserved confounding. When randomised trials are conducted in human populations, estimates of causal effects at the population level can still be biased if there are both effect modification and systematic differences between the trial sample and the ultimate population of inference with respect to these modifiers.
Recent advances in the survey statistics literature to improve inference in nonprobability samples by using information from probability samples can provide an avenue for improving population causal inference in clinical trials when relevant probability samples of the patient population are available. We review some recent work in transporting causal effect estimates from trials to populations and propose a doubly robust estimator of population causal effects that is consistent if either the odds of being the clinical trial versus the population can be correctly estimated, or if the effect modification of the treatment can be correctly estimated.
We explore our proposed approach and compare with some standard existing methods in a simulation study and apply it to a study of pulmonary artery catheterisation in critically ill patients where we believe differences between the trial sample and the larger population might impact overall estimates of treatment effects.
Speaker
Michael Elliott, Department of Biostatistics and Institute for Social Research, University of Michigan
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