Optimizing Complex Health Intervention Packages through the Learn-As-you-GO (LAGO) Design
Abstract
In the face of vast numbers of preventable deaths worldwide and gaping disparities in their distribution, we cannot afford to conduct null and inconclusive effectiveness and implementation trials of evidence-based interventions. The gold standard in biomedical research, the individually randomized clinical trial, is ill-suited as the primary tool for knowledge generation for contextually relevant, scalable, complex public health interventions of multi-component strategies. In this paper, we discuss the new Learn-As-you-GO (LAGO) design. In LAGO trials, the components of a complex intervention package are repeatedly optimized in pre-planned stages, until the package achieves its outcome and power goals with minimized cost and/or other optimization criteria, such as maximizing patient satisfaction. In this paper, the inputs to, and outputs of, LAGO are described, along with its general methodology. The methods are illustrated in the BetterBirth study, a large trial that aimed to reduce maternal and neonatal mortality in Uttar Pradesh, India, using the WHO essential birth practices checklist. Despite its scale, the BetterBirth study failed to demonstrate a significant effect of the intervention package on the primary health endpoint that included maternal mortality. We show how this unfortunate outcome could have been remedied had LAGO been used. LAGO is further illustrated through the discussion of several ongoing LAGO-informed implementation trials of HIV and non-communicable diseases in the United States and Sub-Saharan Africa. The Learn-As-you-GO (LAGO) design optimizes a complex, multi-level intervention for minimum cost, pre-specified power, and a pre-specified effectiveness goal, by adapting the intervention as the study is conducted, reducing risk of trial failure.
Cite
@article{arxiv.2603.06283,
title = {Optimizing Complex Health Intervention Packages through the Learn-As-you-GO (LAGO) Design},
author = {Donna Spiegelman and Dong Roman Xu and Ante Bing and Guangyu Tong and Mona Abdo and Jingyu Cui and Charles Goss and John Baptist Kiggundu and Chris T. Longenecker and LaRon Nelson and Drew Cameron and Fred Semitala and Xin Zhou and Judith J. Lok},
journal= {arXiv preprint arXiv:2603.06283},
year = {2026}
}