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Abstract:
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In the analysis of multi-arm randomized trials, methods for pooling data across trials generally belong to one of two broad classes. The first class of methods consists of contrast-based estimators that estimate the contrast in treatment effect for each pair of treatment levels and then pool across the estimated contrasts. The second class encompasses arm-based methods that construct marginal estimates for each treatment arm and then computes the contrast from the marginal estimates. Leading researchers have assailed arm-based methods under the broad criticism of "breaking randomization", implying biased estimation for population causal effects of treatment. However, to date no one has established a formal causal definition of "breaking randomization", nor a critical examination of the amount of bias that would result. In this talk, I characterize the conditions under which the bias of arm-based methods will (and will not) be biased for population causal effects and also discuss the advantages that arm-based methods have over contrast-based methods with regards to precision.
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