Analysis of “Learn-As-you-GO” (LAGO) in Stepped Wedge Designs with Random Facility Effects

Music City Center 

In implementation science, studies often demand substantial investments of time, money, and personnel but may still fail to detect significant treatment effects. This raises a critical challenge: how to allocate resources efficiently to achieve statistically significant results while minimizing study costs. This work introduces the Learn-As-you-GO (LAGO) design in the context of the widely used yet complex stepped wedge design (SWD). The LAGO approach adapts interventions in a later stage of a study based on data collected from earlier stages, aiming to enhance cost-efficiency. However, this adaptation creates dependencies across stages, clinics, and patients, which challenge classical statistical methods relying on the assumption of independent and identically distributed data. We rigorously demonstrate that classical statistical properties, such as consistency and asymptotic normality, are preserved when analyzing LAGO-generated data. Simulations demonstrate LAGO's advantages in balancing effects and costs over fixed-intervention designs. These findings highlight LAGO's potential to advance implementation science by enabling more impactful and resource-efficient studies.

Adaptive Design

Intervention Adaptation

Stepped Wedge Design

Cost-Efficiency

Asymptotic Normality

Consistency 

Biometrics Section