Time-varying treatment effect models in stepped-wedge cluster-randomized trials with multiple interventions
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多干预阶梯楔形整群随机试验中的时变处理效应模型
- 研究问题
- 当多项干预的效果随暴露时长变化时，阶梯楔形整群随机试验中的常数效应估计量对应什么目标，又会如何影响平均效应估计与区间覆盖？
- 方法与思路
- 作者推导真实效应依赖暴露时长时常数估计量的期望，并研究时变固定效应与随机效应模型，通过并行和析因设计模拟及 PONDER 试验作比较。
- 材料陈述的贡献
- 摘要称，常数估计量对应暴露时长效应的非均匀加权平均；忽略异质性可造成偏差和覆盖不佳，所考察模拟中并行设计的功效通常略高于析因设计。
- 阅读关注点
- 应核对权重如何由试验设计产生、目标平均效应如何定义，以及两种时变模型的约束；应用中还应区分效应证据不足与估计精度差异。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要未给出权重公式和具体模拟条件；试验中未发现处理效应，不能解释为已证明干预无效。
原文摘要
The traditional model specification of stepped-wedge cluster-randomized trials assumes a homogeneous treatment effect across time while adjusting for fixed-time effects. However, when treatment effects vary over time, the constant effect estimator may be biased. In the general setting of stepped-wedge cluster-randomized trials with multiple interventions, we derive the expected value of the constant effect estimator when the true treatment effects depend on exposure time periods. Applying this result to concurrent and factorial stepped wedge designs, we show that the estimator represents a weighted average of exposure-time-specific treatment effects, with weights that are not necessarily uniform across exposure periods. Extensive simulation studies reveal that ignoring time heterogeneity can result in biased estimates and poor coverage of the average treatment effect. In this study, we examine two models designed to accommodate multiple interventions with time-varying treatment effects: (1) a time-varying fixed treatment effect model, which allows treatment effects to vary by exposure time but remain fixed for each time point, and (2) a random treatment effect model, where the time-varying treatment effects are modeled as random deviations from an overall mean. In the simulations considered in this study, concurrent designs generally achieve higher power than factorial designs under a time-varying fixed treatment effect model, though the differences are modest. Finally, we apply the constant effect model and both time-varying treatment effect models to data from the Prognosticating Outcomes and Nudging Decisions in the Electronic Health Record (PONDER) trial. All three models indicate a lack of treatment effect for either intervention, though they differ in the precision of their estimates, likely due to variations in modeling assumptions.