Sensitivity analysis in longitudinal clinical trials via distributional imputation
通过分布插补开展纵向临床试验敏感性分析AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确评估治疗效应的缺失数据敏感性，并报告分位数处理效应，支持因果推断及分布效应分类。
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通过分布插补开展纵向临床试验敏感性分析
- 研究问题
- 纵向临床试验存在缺失数据、随机缺失假设又不可验证时，如何针对不同预设插补模型评估治疗效应结论的敏感性并可靠推断？
- 方法与思路
- 作者提出分布插补，从给定观测数据的目标插补模型抽样，以蒙特卡洛积分思路求解平均估计方程，并用加权自助法估计方差。
- 材料陈述的贡献
- 摘要称，方法具有完整效率的理论保证和一致方差估计；抗抑郁试验中，若干预设敏感性模型下的平均、风险差及低分位效应显著。
- 阅读关注点
- 应核对效率结论的参照与条件、目标插补模型的含义、参数估计不确定性的传递，以及各敏感性模型和分位数效应的具体结果。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审及期刊接收未经核实；摘要未列出全部模型与数值结果，显著性只限于所述预设敏感性分析。
原文摘要
Missing data is inevitable in longitudinal clinical trials. Conventionally, the missing at random assumption is assumed to handle missingness, which however is unverifiable empirically. Thus, sensitivity analysis is critically important to assess the robustness of the study conclusions against untestable assumptions. Toward this end, regulatory agencies often request using imputation models such as return-to-baseline, control-based, and washout imputation. Multiple imputation is popular in sensitivity analysis; however, it may be inefficient and result in an unsatisfying interval estimation by Rubin's combining rule. We propose distributional imputation (DI) in sensitivity analysis, which imputes each missing value by samples from its target imputation model given the observed data. Drawn on the idea of Monte Carlo integration, the DI estimator solves the mean estimating equations of the imputed dataset. It is fully efficient with theoretical guarantees. Moreover, we propose weighted bootstrap to obtain a consistent variance estimator, taking into account the variabilities due to model parameter estimation and target parameter estimation. The finite-sample performance of DI inference is assessed in the simulation study. We apply the proposed framework to an antidepressant longitudinal clinical trial involving missing data to investigate the robustness of the treatment effect. Our proposed DI approach detects a statistically significant treatment effect in both the primary analysis and sensitivity analysis under certain prespecified sensitivity models in terms of the average treatment effect, the risk difference, and the quantile treatment effect in lower quantiles of the responses, uncovering the benefit of the test drug for curing depression.