Researchers conducting randomized trials have increasingly shifted focus from the average treatment effect to understanding moderators of treatment effects. Current methods for exploring moderation focus on model selection and hypothesis tests. At the same time, recent developments in the design of randomized trials have argued for the need for population-based recruitment in order to generalize well. In this paper, we show that a different population-based recruitment strategy can be implemented to increase the precision of estimates of treatment effect moderators, and we explore the trade-offs between optimal designs for the average treatment effect and moderator effects.
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Elizabeth Tipton (2020) studied this question.
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