Robust analyses for longitudinal clinical trials with missing and non-normal continuous outcomes
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缺失且非正态连续结局的纵向临床试验稳健分析
- 研究问题
- 纵向临床试验存在结局缺失、异常值或厚尾分布时，如何在基于对照组的插补假设下稳健估计平均处理效应？
- 方法与思路
- 作者用序贯加权稳健回归建立插补模型，结合均值插补和稳健分析，并以相同历史的对照组结局刻画缺失后的轨迹。
- 材料陈述的贡献
- 摘要称，估计量具有一致性和渐近正态性，且分析模型误设时相关渐近结论仍成立；模拟和临床应用展示其表现。
- 阅读关注点
- 应核对基于对照组的缺失假设、稳健回归权重和插补步骤，以及分析模型误设稳健性的范围与方差估计方法。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已经同行评审或期刊接收；摘要未列出理论条件，不能把分析模型稳健性推广至任意缺失机制。
原文摘要
Missing data is unavoidable in longitudinal clinical trials, and outcomes are not always normally distributed. In the presence of outliers or heavy-tailed distributions, the conventional multiple imputation with the mixed model with repeated measures analysis of the average treatment effect (ATE) based on the multivariate normal assumption may produce bias and power loss. Control-based imputation (CBI) is an approach for evaluating the treatment effect under the assumption that participants in both the test and control groups with missing outcome data have a similar outcome profile as those with an identical history in the control group. We develop a general robust framework to handle non-normal outcomes under CBI without imposing any parametric modeling assumptions. Under the proposed framework, sequential weighted robust regressions are applied to protect the constructed imputation model against non-normality in both the covariates and the response variables. Accompanied by the subsequent mean imputation and robust model analysis, the resulting ATE estimator has good theoretical properties in terms of consistency and asymptotic normality. Moreover, our proposed method guarantees the analysis model robustness of the ATE estimation, in the sense that its asymptotic results remain intact even when the analysis model is misspecified. The superiority of the proposed robust method is demonstrated by comprehensive simulation studies and an AIDS clinical trial data application.