Analysis of "Learn-As-You-Go" (LAGO) Studies

TL;DR

This paper develops statistical methods for analyzing learn-as-you-go (LAGO) adaptive studies, enabling valid effect estimation and confidence inference despite the complex, stage-dependent intervention design.

Contribution

It introduces novel estimation and inference techniques for LAGO studies, ensuring validity despite the adaptive, stage-dependent intervention process.

Findings

Proved consistency and asymptotic normality of estimators.

Developed confidence sets for optimal intervention packages.

Applied methods successfully in the BetterBirth Study.

Abstract

In learn-as-you-go (LAGO) adaptive studies, the intervention is a complex package consisting of multiple components, and is adapted in stages during the study based on past outcome data. This design formalizes standard practice, and desires for practice, in public health intervention studies. An effective intervention package is sought, while minimizing intervention package cost. When analyzing data from a learn-as-you-go study, the interventions in later stages depend upon the outcomes in the previous stages, violating standard statistical theory. We develop methods for estimating the intervention effects in a LAGO study. We prove consistency and asymptotic normality using a novel coupling argument, ensuring the validity of the test for the hypothesis of no overall intervention effect. We develop a confidence set for the optimal intervention package and confidence bands for the success probabilities under alternative package compositions. We illustrate our methods in the BetterBirth Study, which aimed to improve maternal and neonatal outcomes among 157,689 births in Uttar Pradesh, India through a complex, multi-component intervention package.