Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity
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评估处理效应异质性的最大最小最优整群随机设计
- 研究问题
- 在预算受限且协变量与结果的组内相关系数未知时，如何配置群组数量和每组人数，以提高预设异质性效应分析的效率？
- 方法与思路
- 作者先推导给定相关系数的局部最优设计，再构造最大化最坏情形相对效率的设计，并联合考虑平均与异质性效应。
- 材料陈述的贡献
- 摘要提供异质性效应分析的设计公式、稳健资源配置和多目标设计，并以糖尿病预防试验说明方法及提供 R Shiny 工具。
- 阅读关注点
- 应核对预算成本结构、相关系数不确定范围和异质性参数定义，以及最坏情形效率与平均效应目标之间的权衡。
- 阅读边界
- 所给版本为 arXiv 预印本，不能确认同行评审或期刊接收；摘要未给出设计公式所需模型条件、参数范围或具体效率增益。
原文摘要
Cluster randomized trials (CRTs) are studies where treatment is randomized at the cluster level but outcomes are typically collected at the individual level. When CRTs are employed in pragmatic settings, baseline population characteristics may moderate treatment effects, leading to what is known as heterogeneous treatment effects (HTEs). Pre-specified, hypothesis-driven HTE analyses in CRTs can enable an understanding of how interventions may impact subpopulation outcomes. While closed-form sample size formulas have recently been proposed, assuming known intracluster correlation coefficients (ICCs) for both the covariate and outcome, guidance on optimal cluster randomized designs to ensure maximum power with pre-specified HTE analyses has not yet been developed. We derive new design formulas to determine the cluster size and number of clusters to achieve the locally optimal design (LOD) that minimizes variance for estimating the HTE parameter given a budget constraint. Given the LODs are based on covariate and outcome-ICC values that are usually unknown, we further develop the maximin design for assessing HTE, identifying the combination of design resources that maximize the relative efficiency of the HTE analysis in the worst case scenario. In addition, given the analysis of the average treatment effect is often of primary interest, we also establish optimal designs to accommodate multiple objectives by combining considerations for studying both the average and heterogeneous treatment effects. We illustrate our methods using the context of the Kerala Diabetes Prevention Program CRT, and provide an R Shiny app to facilitate calculation of optimal designs under a wide range of design parameters.