PDF(Pubmed)
Abstract：
Recently, there has been considerable progress in developing new technologies and equipment for the medical field, including minimally invasive surgeries. Evaluating the effectiveness of these treatments requires study designs like randomized controlled trials. However, due to the nature of certain treatments, randomization is not always feasible, leading to the use of observational studies. The effect size estimated from observational studies is subject to selection bias caused by confounders. One method to reduce this bias is propensity scoring. This study aimed to introduce a propensity score matching process between two groups using a practical example with R. Additionally, Rex, an Excel add-in graphical user interface statistical program, is provided for researchers unfamiliar with R programming. Further techniques, such as matching with three or more groups, propensity score weighting and stratification, and imputation of missing values, are summarized to offer approaches for more complex studies not covered in this tutorial.
摘要：
最近,在开发医疗领域的新技术和设备方面取得了长足的进步，包括微创手术.评估这些治疗的有效性需要研究设计，如随机对照试验。然而,由于某些治疗的性质，随机化并不总是可行的，导致观察性研究的使用。从观察性研究中估计的效应大小受到混杂因素造成的选择偏差的影响。减少这种偏差的一种方法是倾向评分。本研究旨在使用R的实际例子在两组之间引入倾向得分匹配过程。雷克斯,Excel插件图形用户界面统计程序，提供给不熟悉R编程的研究人员。进一步的技术，例如与三个或更多的组匹配，倾向得分加权和分层，以及缺失值的估算，被总结为提供本教程中未涵盖的更复杂研究的方法。
