标题： 缺失数据填补在倾向得分分析背景下的系统综述 显示英文标题

标题： Missing Data Imputation in the Context of Propensity Score Analysis: A Systematic Review

摘要： 缺失数据是观察性研究中的常见挑战。 另一个挑战来自于研究本身的观察性质。 在此情况下，倾向评分分析可以作为一种技术，以复制临床试验中发现的类似条件。 关于缺失数据，大多数研究仅分析完整案例，但这存在多个问题。 在本综述中，我们调查了在倾向评分分析背景下用于处理缺失数据的方法。 因此，我们在PubMed上搜索了“倾向评分”和“缺失数据”这两个关键词，并将搜索范围限制在2010年1月至2024年2月之间。 本综述遵循了PRISMA声明。 共有147篇文章被纳入分析。 本研究的主要发现是，尽管多重插补（MI）的使用随着时间的推移有所增加，但只有少数研究描述了缺失数据的机制以及MI算法的细节。 关键词 缺失数据，倾向评分，观察数据，多重插补，系统综述 显示英文摘要

摘要： Missing data is a common challenge in observational studies. Another challenge stems from the observational nature of the study itself. Here, propensity score analysis can be used as a technique to replicate conditions similar to those found in clinical trials. With regard to the missing data, a majority of studies only analyze the complete cases, but this has several pitfalls. In this review, we investigate which methods are used for the handling of missing data in the context of propensity score analyses. Therefore, we searched PubMed for the keywords propensity score and missing data, restricting our search to the time between January 2010 and February 2024. The PRISMA statement was followed in this review. A total of 147 articles were included in the analyses. A major finding of this study is that although the usage of multiple imputation (MI) has risen over time, only a limited number of studies describe the mechanism of missing data and the details of the MI algorithm. Keywords Missing data, Propensity Score, Observational Data, Multiple Imputation, Systematic Review