A Framework for Parametric Time-Varying Treatment Effects in Multiple Intervention Stepped Wedge Design Clinical Trials in the Presence of Non-Uniform Cluster-Period Correlation Structures
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非均匀簇期相关下多干预阶梯楔形试验的参数化时变效应框架
- 研究问题
- 多干预阶梯楔形试验中，随暴露时间变化的效应与非均匀簇期相关如何影响目标参数、设计功效及模型误设偏误？
- 方法与思路
- 作者把参数化暴露响应函数纳入修正的固定效应设计矩阵，通过广义最小二乘推导效应目标、方差、功效与偏误。
- 材料陈述的贡献
- 摘要称时变效应可能使所需簇数成倍增加，误设可引发较大偏误，且对主效应和交互效应的影响存在差异。
- 阅读关注点
- 应核对暴露响应函数、簇期相关结构及交叉安排，辨明目标效应如何随设定变化，并审查功效与误设分析的场景。
- 阅读边界
- 这是预印本，同行评审与期刊接收均未核实。摘要未提供具体函数、相关参数和数值场景，不能直接用于确定试验样本量。
原文摘要
Stepped wedge design (SWD) trials typically assume that treatment effects are immediate following implementation, but this assumption is often unrealistic in pragmatic settings where effects evolve over exposure time. This impact is further complicated in multiple-intervention stepped wedge designs (M-SWDs), due to their complex crossover patterns. Existing work has addressed time-varying treatment effects for main effects at the analysis stage using nonparametric approaches; however, the implications for design-stage power and estimand interpretation in M-SWDs remain largely unaddressed. We develop a unified framework that incorporates parametric exposure-response functions into a modified fixed-effects design matrix, Z*, and derive corresponding generalized least squares (GLS) expressions for treatment effect estimands, variance, power, and bias under model misspecification. Analytic and simulation results demonstrate that time-varying effects can require multiple-fold increase in the number of clusters required to achieve the nominal target power. Misspecification can also induce large bias in treatment effect estimates, with particularly pronounced and non-uniform impacts on interaction effects. These findings demonstrate that treatment effect estimands in M-SWDs depend critically on assumptions about exposure-time dynamics and that failure to account for time-varying effects can lead to substantial miscalibration of power and biased inference, affecting main and interaction effects differently.