Learn-As-you-GO (LAGO) Trials: Optimizing Treatments and Preventing Trial Failure Through Ongoing Learning

TL;DR

This paper develops methods for ongoing adaptation of intervention packages in public health trials, ensuring valid inference and optimal treatment selection despite modifications during the trial.

Contribution

It extends the theory of Learn-As-you-GO (LAGO) to continuous outcomes and provides estimators and confidence sets for adaptive intervention trials.

Findings

Derived point and interval estimators for intervention effects.

Ensured validity of hypothesis tests during adaptive interventions.

Developed confidence sets for optimal intervention packages.

Abstract

It is well known that changing the intervention package while a trial is ongoing does not lead to valid inference using standard statistical methods. However, it is often necessary to adapt, tailor, or tweak a complex intervention package in public health implementation trials, especially when the intervention package does not have the desired effect. This article presents conditions under which the resulting analyses remain valid even when the intervention package is adapted while a trial is ongoing. Our results on such Learn-As-you-GO (LAGO) studies extend the theory of LAGO for binary outcomes following a logistic regression model (Nevo, Lok and Spiegelman, 2021) to LAGO for continuous outcomes under flexible conditional mean model. We derive point and interval estimators of the intervention effects and ensure the validity of hypothesis tests for an overall intervention effect. We develop a confidence set for the optimal intervention package, which achieves a pre-specified mean outcome while minimizing cost, and confidence bands for the mean outcome under all intervention package compositions. This work will be useful for the design and analysis of large-scale intervention trials where the intervention package is adapted, tailored, or tweaked while the trial is ongoing.