Covariate Adjustment in Randomized Clinical Trials with Missing Covariate and Outcome Data
协变量和结果缺失时随机临床试验的协变量调整AI 解读 ↓
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协变量和结果缺失时随机临床试验的协变量调整
- 研究问题
- 随机临床试验中的基线协变量或结果缺失，会如何影响平均处理效应估计，怎样进行协变量调整以改善估计精度？
- 方法与思路
- 作者回顾协变量缺失时的调整方法，并在结果随机缺失情形结合逆概率加权与重叠加权，强调缺失指示与协变量交互项。
- 材料陈述的贡献
- 摘要提出完整加权方案，并报告在调整变量与结果相关时，所考察方法通常提高精度；另给出模拟比较及儿童临床试验应用。
- 阅读关注点
- 应核对不同缺失机制对应的识别条件、权重构造、交互项作用，以及连续和二元结果下的估计目标与有限样本表现。
- 阅读边界
- 这是 arXiv 预印本，未核实同行评审或期刊接收。摘要未给出完整缺失假设及适用条件，精度改善不能推广到任意缺失机制。
原文摘要
When analyzing data from randomized clinical trials, covariate adjustment can be used to account for chance imbalance in baseline covariates and to increase precision of the treatment effect estimate. A practical barrier to covariate adjustment is the presence of missing data. In this paper, in the light of recent theoretical advancement, we first review several covariate adjustment methods with incomplete covariate data. We investigate the implications of the missing data mechanism on estimating the average treatment effect in randomized clinical trials with continuous or binary outcomes. In parallel, we consider settings where the outcome data are fully observed or are missing at random; in the latter setting, we propose a full weighting approach that combines inverse probability weighting for adjusting missing outcomes and overlap weighting for covariate adjustment. We highlight the importance of including the interaction terms between the missingness indicators and covariates as predictors in the models. We conduct comprehensive simulation studies to examine the finite-sample performance of the proposed methods and compare with a range of common alternatives. We find that conducting the proposed adjustment methods generally improves the precision of treatment effect estimates regardless of the imputation methods when the adjusted covariate is associated with the outcome. We apply the methods to the Childhood Adenotonsillectomy Trial to assess the effect of adenotonsillectomy on neurocognitive functioning scores.