Addressing Confounding by Indication Through (Un)Measured Centre Characteristics in Learn-As-you-GO(LAGO) Trials

TL;DR

This paper extends the LAGO adaptive trial design to control for confounding by center characteristics, including unmeasured ones, using fixed effects, applicable to various outcome types.

Contribution

It introduces fixed center effects into LAGO theory, ensuring valid inference even with unmeasured confounders and small numbers of centers.

Findings

Established asymptotic properties for continuous and binary outcomes.

Derived point and interval estimators with proven consistency and normality.

Provided valid hypothesis tests and optimization methods for intervention packages.

Abstract

The Learn-As-you-Go (LAGO) design is an adaptive clinical trial design that allows modifications to multicomponent intervention packages across stages. Centers participate in more than one stage, as is common in large-scale implementation trials. In LAGO trials, center characteristics may act as confounders, predicting both the intervention package and the outcomes. We extend the LAGO theory by introducing fixed center effects to control for confounding by indication through measured and unmeasured center characteristics. Conditioning on center characteristics by including fixed center effects ensures asymptotic results hold without requiring explicit characterization of unmeasured confounders. Our methods apply even with small numbers of centers. LAGO theory is established for continuous outcomes following a generalized linear model and binary outcomes following a logistic regression model, unifying theory across outcome types. Point- and interval estimators are derived, and consistency and asymptotic normality are established. Valid hypothesis tests for the overall intervention effect are provided, and the optimal intervention package minimizing cost subject to a target outcome mean is obtained via constrained optimization.