A Bayesian Framework for Latent Compliance Modeling in Cluster Randomized Trials with One-Sided Noncompliance
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单侧不依从聚类随机试验中的潜在依从性贝叶斯建模
- 研究问题
- 当医疗机构实施程度与患者依从行为共同影响干预接受时，如何刻画实施异质性，并估计总体及类型内的治疗效应？
- 方法与思路
- 作者用潜在混合模型归纳机构实施类型，联合建模个体依从与结局，并填补对照组不可观测的机构及个体依从行为。
- 材料陈述的贡献
- 摘要称，该框架可估计有限总体和超总体的 ITT 与 CACE，兼顾总体及潜在类型差异，并展示于 METRIcAL 试验。
- 阅读关注点
- 应核对潜在实施类型的可识别性、对照组依从填补的依据、先验敏感性，以及类型内 CACE 所需的因果假设。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未获核实；摘要未说明完整识别约束，不能认定随机化足以识别全部潜在类型效应。
原文摘要
In pragmatic cluster randomized controlled trials (PCRCTs), healthcare providers are randomized while both providers and patients may deviate from the assigned intervention. In many PCRCTs, cluster-level implementation is measured using multiple continuous metrics, while individual compliance is recorded as a binary indicator. Standard complier average causal effect (CACE) estimands focus on individual-level compliance and do not account for heterogeneity in implementation across clusters. When intervention uptake is shaped by both provider- and patient-level processes, it is of scientific interest to characterize how effects vary across these sources of compliance. We propose a Bayesian framework for PCRCTs with one-sided binary noncompliance at the individual level and one-sided partial compliance at the cluster level. The method uses a latent mixture model to summarize heterogeneity in cluster-level implementation based on baseline characteristics and observed implementation measures, and links these latent implementation types to individual compliance and outcomes through a joint model. Because compliance is only observed in treated clusters, the model imputes unobserved compliance behavior for clusters and individuals assigned to control. The framework enables estimation of finite- and super-population intent-to-treat (ITT) and CACE estimands, both marginally and within latent implementation types. We apply the method to the METRIcAL trial, a pragmatic cluster randomized study evaluating a personalized music intervention for nursing home residents with dementia. The analysis illustrates how accounting for implementation heterogeneity and individual compliance can provide insights beyond standard ITT analyses.}{Causal inference; Principal stratification; Complier average causal effect; Cluster randomized trials; Noncompliance; Bayesian methods; Latent variable models; Interference.