Bayesian analysis of the causal reference-based model for missing data in clinical trials
临床试验缺失数据的因果参照模型贝叶斯分析AI 解读 ↓
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临床试验缺失数据的因果参照模型贝叶斯分析
- 研究问题
- 临床试验中伴发事件导致后续结果缺失时，如何将事件发生后治疗效应保留程度的不确定性纳入参照插补及治疗策略效应推断？
- 方法与思路
- 作者在含治疗效应保留参数的因果参照模型中为该参数设置先验，使贝叶斯推断明确反映参照假设的不确定性，并开展模拟与试验应用。
- 材料陈述的贡献
- 摘要提出在事件后数据很少或缺失时仍纳入效应保留假设不确定性的分析方法，并将其频率学性质与既有参照方法进行比较。
- 阅读关注点
- 应核对效应保留参数的因果含义、先验敏感性、缺失机制与可识别性，以及后验不确定性如何对应重复抽样方差和覆盖率。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要未报告比较结果；事件后数据不足时，先验和参照假设对推断的影响需核对。
原文摘要
The statistical analysis of clinical trials is often complicated by missing data. Patients sometimes experience intercurrent events (ICEs), which usually (although not always) lead to missing subsequent outcome measurements for such individuals. The reference-based imputation methods were proposed by Carpenter et al. (2013) and have been commonly adopted for handling missing data due to ICEs when estimating treatment policy strategy estimands. Conventionally, the variance for reference-based estimators was obtained using Rubin's rules. However, Rubin's rules variance estimator is biased compared to the repeated sampling variance of the point estimator, due to uncongeniality. Repeated sampling variance estimators were proposed as an alternative to variance estimation for reference-based estimators. However, these have the property that they decrease as the proportion of ICEs increases. White et al. (2019) introduced a causal model incorporating the concept of a 'maintained treatment effect' following the occurrence of ICEs and showed that this causal model included common reference-based estimators as special cases. Building on this framework, we propose introducing a prior distribution for the maintained effect parameter to account for uncertainty in this assumption. Our approach provides inference for reference-based estimators that explicitly reflects our uncertainty about how much treatment effects are maintained after the occurrence of ICEs. In trials where no or little post-ICE data are observed, our proposed Bayesian reference-based causal model approach can be used to estimate the treatment policy treatment effect, incorporating uncertainty about the reference-based assumption. We compare the frequentist properties of this approach with existing reference-based methods through simulations and by application to an antidepressant trial.