ANALYSIS OF LEARN-AS-YOU-GO (LAGO) STUDIES

• 成果类型: Article
• 署名作者: Nevo, Daniel; Lok, Judith J.; Spiegelman, Donna
• 署名单位: Tel Aviv University; Boston University; Yale University; Yale University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/20-AOS1978
• 发表日期: 2021
• 页码: 793-819
• 关键词: i clinical-trials; multiple assignment; designs; optimization; EXISTENCE; time

摘要

In Learn-As-you-GO (LAGO) adaptive studies, the intervention is a complex multicomponent package, and is adapted in stages during the study based on past outcome data. This design formalizes standard practice in public health intervention studies. An effective intervention package is sought, while minimizing intervention package cost. In LAGO study data, the interventions in later stages depend upon the outcomes in the previous stages, violating standard statistical theory. We develop an estimator for the intervention effects, and 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 Better-Birth Study, which aimed to improve maternal and neonatal outcomes among 157,689 births in Uttar Pradesh, India through a multicomponent intervention package.