Bayesian inference for cluster-randomized trials with multivariate outcomes subject to both truncation by death and missingness
同时存在死亡截断与缺失的多元结局整群随机试验贝叶斯推断AI 解读 ↓
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同时存在死亡截断与缺失的多元结局整群随机试验贝叶斯推断
- 研究问题
- 当整群随机试验同时存在死亡截断、非死亡失访和生存状态未知时，如何联合分析多元结局并区分个体与群组层面的存活者平均因果效应？
- 方法与思路
- 作者提出贝叶斯多元结局框架，联合处理不同生存状态及缺失类型，在后验中区分两种层级的存活者效应，并开展模拟和老年试验应用。
- 材料陈述的贡献
- 摘要称模型在若干模拟情境中具有低偏差和高覆盖率，并可从双变量连续结局扩展至更多结局或二元结局；具体扩展结果未列出。
- 阅读关注点
- 应核对存活者效应的定义和识别限制、各类缺失机制及先验设定，并查看生存状态未知时的敏感性分析和群组权重处理。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要未说明识别假设、缺失机制条件和覆盖率数值，建模框架本身不保证所有情境下有效。
原文摘要
Cluster-randomized trials (CRTs) on fragile populations frequently encounter complex attrition problems where the reasons for missing outcomes can be heterogeneous, with participants who are known alive, known to have died, or with unknown survival status, and with complex and distinct missing data mechanisms for each group. Although existing methods have been developed to address death truncation in CRTs, no existing methods can jointly accommodate participants who drop out for reasons unrelated to mortality or serious illnesses, or those with an unknown survival status. This paper proposes a Bayesian framework for estimating survivor average causal effects in CRTs while accounting for different types of missingness. Our approach uses a multivariate outcome that jointly estimates the causal effects, and in the posterior estimates, we distinguish the individual-level and the cluster-level survivor average causal effect. We perform simulation studies to evaluate the performance of our model and found low bias and high coverage on key parameters across several different scenarios. We use data from a geriatric CRT to illustrate the use of our model. Although our illustration focuses on the case of a bivariate continuous outcome, our model is straightforwardly extended to accommodate more than two endpoints as well as other types of endpoints (e.g., binary). Thus, this work provides a general modeling framework for handling complex missingness in CRTs and can be applied to a wide range of settings with aging and palliative care populations.