针对同时受死亡截断和缺失影响的多变量结局的整群随机试验的贝叶斯推断
Bayesian inference for cluster-randomized trials with multivariate outcomes subject to both truncation by death and missingness
AI总结：
本文提出一个贝叶斯框架，通过多变量结局联合建模，在整群随机试验中同时处理死亡截断、异质性失访和未知生存状态等复杂缺失问题，并区分个体与整群水平的幸存者平均因果效应。
摘要：
针对脆弱人群的整群随机试验（CRTs）经常遇到复杂的失访问题，其中结局缺失的原因可能具有异质性：参与者可能已知存活、已知死亡或生存状态未知，且每组具有复杂且不同的缺失数据机制。尽管已有方法被开发用于处理CRTs中的死亡截断问题，但尚无现有方法能够同时容纳因与死亡或严重疾病无关的原因而退出研究的参与者，或生存状态未知的参与者。本文提出一个贝叶斯框架，用于在考虑不同类型缺失的情况下估计CRTs中的幸存者平均因果效应。我们的方法使用多变量结局来联合估计因果效应，并在后验估计中区分个体水平和整群水平的幸存者平均因果效应。我们进行了模拟研究以评估模型的性能，发现在多个不同情景下关键参数具有低偏倚和高覆盖率。我们使用一项老年医学CRT的数据来说明我们模型的应用。尽管我们的示例聚焦于双变量连续结局的情形，但我们的模型可以直接扩展以容纳两个以上的终点以及其他类型的终点（例如二分类）。因此，这项工作为处理CRTs中复杂缺失提供了一个通用的建模框架，并可应用于老龄化和姑息治疗人群的广泛场景。
英文摘要：
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.
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