Comparing Propensity Score-Based Methods in Estimating the Treatment Effects: A Simulation Study
倾向得分处理效应估计方法的模拟比较AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要比较观察性研究中控制混杂的倾向得分方法，并以平均处理效应和处理组平均处理效应为目标，直接属于因果推断。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
倾向得分处理效应估计方法的模拟比较
- 研究问题
- 在协变量与处理分配关系逐渐复杂的观察性研究中，倾向得分匹配、分层和逆概率加权的估计表现及结果适用范围有何差异？
- 方法与思路
- 作者构造七种复杂度递增的模拟场景，使平均处理效应与处理组平均处理效应相同，再比较多种倾向得分方法的估计表现。
- 材料陈述的贡献
- 摘要称逆概率加权的估计更接近期望值，而匹配和分层可明确最终估计所对应的实际子样本，从而增强对结果适用范围的控制。
- 阅读关注点
- 应核对七种数据生成机制、倾向得分模型设定、重叠程度与权重处理，以及两种效应相同的设计如何影响方法间的公平比较。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收状态未核实；摘要未报告误差和区间覆盖率，模拟中的加权优势不能视为普遍结论。
原文摘要
In observational studies, the recorded treatment assignment is not purely random, but it is influenced by external factors such as patient characteristics, reimbursement policies, and existing guidelines. Therefore, the treatment effect can be estimated only after accounting for confounding factors. Propensity score (PS) methods are a family of methods that is widely used for this purpose. Although they are all based on the estimation of the a posteriori probability of treatment assignment given patient covariates, they estimate the treatment effect from different statistical points of view and are, thus, relatively hard to compare. In this work, we propose a simulation experiment in which a hypothetical cohort of subjects is simulated in seven scenarios of increasing complexity of the associations between covariates and treatment, but where the two main definitions of treatment effect (average treatment effect, ATE, and average effect of the treatment on the treated, ATT) coincide. Our purpose is to compare the performance of a wide array of PS-based methods (matching, stratification, and inverse probability weighting) in estimating the treatment effect and their robustness in different scenarios. We find that inverse probability weighting provides estimates of the treatment effect that are closer to the expected value by weighting all subjects of the starting population. Conversely, matching and stratification ensure that the subpopulation that generated the final estimate is made up of real instances drawn from the starting population, and, thus, provide a higher degree of control on the validity domain of the estimates.