Analysis of "Learn-As-You-Go" (LAGO) Studies
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In learn-as-you-go (LAGO) adaptive designs, the intervention is a package consisting of multiple components, and is adapted in stages during the study based on past outcomes. This formalizes standard practice, and desires for practice, in public health intervention studies. Typically, an effective intervention package is sought, while minimizing cost. The main complication when analyzing data from a learn-as-you-go design is that interventions in later stages depend upon the outcomes in the previous stages. Therefore, conditioning on the interventions would lead to effectively conditioning on the earlier stages' outcomes, which violates common statistical principles. We develop a method to estimate intervention effects from a learn-as-you-go study. We prove consistency and asymptotic normality using a novel coupling argument, and ensure the validity of the test for the hypothesis of no overall intervention effect. We further develop a confidence set for the optimal intervention package and confidence bands for the success probabilities under different package compositions. We illustrate our methods by applying them to the BetterBirth Study, which aimed to improve maternal and neonatal outcomes in India.
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