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.

翻译：面对全球范围内大量可预防的死亡及其分布上的巨大差异，我们无法承受对循证干预措施进行无效且无定论的有效性与实施性试验。生物医学研究中的金标准——个体随机临床试验——并不适合作为针对多组分策略、具有情境相关性、可扩展的复杂公共卫生干预措施的主要知识生成工具。本文讨论了一种新的“边学边做”（LAGO）设计。在LAGO试验中，复杂干预方案的组成部分会在预先规划的阶段中反复优化，直至该方案以最小化的成本和/或其他优化标准（例如最大化患者满意度）达成其结局和功效目标。本文描述了LAGO的输入与输出及其通用方法。这些方法在BetterBirth研究中得到说明，这是一项旨在通过使用世界卫生组织基本分娩实践清单来降低印度北方邦孕产妇和新生儿死亡率的大型试验。尽管规模庞大，但BetterBirth研究未能证明干预方案对包含孕产妇死亡率在内的主要健康终点具有显著效果。我们展示了如果使用LAGO，本可以如何补救这一不幸的结果。通过讨论美国及撒哈拉以南非洲地区几项正在进行的、基于LAGO理念的HIV和非传染性疾病实施试验，进一步阐释了LAGO。“边学边做”（LAGO）设计通过在研究进行过程中调整干预措施，以最小化成本、预先设定的功效和预先设定的有效性目标来优化复杂的多层次干预方案，从而降低试验失败的风险。