Adaptive clinical trial designs have gained popularity, allowing for modifications to sample sizes, participant populations, treatment arm selection, and other parameters. However, existing adaptive trial designs do not address changes to the intervention packages themselves, which have a reputation for invalidating statistical inferences. This has motivated the development of Learn-As-you-GO (LAGO), an adaptive clinical trial design that allows for modifications to multicomponent intervention packages over different stages. Centre characteristics might be confounders, predicting both the intervention package implemented and the outcomes in the centres. This work extends LAGO theory by using fixed centre effects to control for confounding by indication through both measured and unmeasured centre-specific characteristics. We show that the fixed centre effects provide reliable control for centre-level confounding even with small numbers of centres, demonstrating the applicability of this LAGO design across various trial settings. We also extend LAGO to allow centres to participate in more than one stage, which is realistic in large-scale implementation trials. Point and interval estimators for the intervention effects are derived. Consistency and asymptotic normality of the intervention effect estimators are established. Moreover, we provide valid hypothesis tests for the overall intervention effect. The optimal intervention package achieving a predetermined outcome mean while minimizing cost is estimated through constrained optimization.

翻译：适应性临床试验设计日益流行，允许调整样本量、受试人群、治疗臂选择及其他参数。然而，现有适应性试验设计无法应对干预包本身的改变，而这通常被认为会破坏统计推断的有效性。这促进了“边学习边调整”（LAGO）适应性临床试验设计的发展，该设计允许在不同阶段修改多组件干预包。中心特征可能成为混杂因素，同时预测中心实施的干预包及其结局。本研究通过使用固定中心效应控制可测量和不可测量的中心特异性特征引起的适应症混杂，从而扩展了LAGO理论。我们证明即便在中心数量较少的情况下，固定中心效应也能可靠地控制中心层面混杂，表明该LAGO设计适用于多种试验场景。我们还扩展了LAGO以允许中心参与多个阶段，这在大规模实施试验中更切合实际。推导了干预效应的点估计与区间估计方法，建立了干预效应估计量的一致性与渐近正态性。此外，我们为总体干预效应提供了有效的假设检验。通过约束优化估计了在预设结局均值下实现成本最小化的最优干预包。