Confidence Intervals for the Risk Difference in Combined Unilateral and Bilateral Data Incorporating a Distribution-Based Approach
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结合分布方法的单侧与双侧混合数据风险差置信区间
- 研究问题
- 对于同时包含单侧和双侧器官二元结局的数据，如何构造风险差区间，兼顾个体内相关性与小样本抽样分布的偏斜？
- 方法与思路
- 作者从风险差估计量的概率分布构造置信区间，并提出考虑个体内相关性的修正 MOVER 程序，与常用渐近方法比较。
- 材料陈述的贡献
- 摘要称分布区间在模拟中覆盖率接近名义水平、宽度与既有方法相近，且能反映小样本偏斜；两个真实数据分析结论一致。
- 阅读关注点
- 应核对单侧与双侧观测的联合分布、相关参数处理，以及分布区间和修正 MOVER 的构造步骤与小样本比较设置。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；摘要未给出分布假设和样本量范围，治疗组风险差的区间估计不自动建立因果识别。
原文摘要
Combined unilateral and bilateral binary outcomes frequently arise in studies involving paired organs. The risk difference is a clinically interpretable measure for comparing treatment effects between groups. Existing confidence interval methods are primarily based on asymptotic normality and may fail to adequately reflect finite-sample distributional features, particularly skewness. To address this issue, we propose a distribution-based confidence interval derived from the probability distribution of the risk difference estimator and a modified MOVER procedure that accounts for intra-subject correlation. Their performances are compared with those of commonly used asymptotic methods through extensive simulation studies. Across a broad range of parameter settings, all methods exhibited satisfactory performance as sample size increased. The proposed distribution-based interval achieved coverage probabilities close to the nominal level with interval widths comparable to those of existing procedures. In small sample settings, it was able to capture skewness in the sampling distribution that was not reflected by methods relying on asymptotic normality. Analyses of two real-world datasets demonstrated the practical applicability of the competing methods and yielded consistent inferential conclusions. The proposed approach provides an alternative framework for interval estimation of the risk difference in studies involving combined unilateral and bilateral binary outcomes.