Malla Lucas, Perera-Salazar Rafael, McFadden Emily, Ogero Morris, Stepniewska Kasia, English Mike
Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
J Comp Eff Res. 2018 Mar;7(3):271-279. doi: 10.2217/cer-2017-0071. Epub 2017 Oct 5.
Even though systematic reviews have examined how aspects of propensity score methods are used, none has reviewed how the challenge of missing data is addressed with these methods. This review therefore describes how missing data are addressed with propensity score methods in observational comparative effectiveness studies.
Published articles on observational comparative effectiveness studies were extracted from MEDLINE and EMBASE databases.
Our search yielded 167 eligible articles. Majority of these studies (114; 68%) conducted complete case analysis with only 53 of them stating this in the methods. Only 16 articles reported use of multiple imputation.
Few researchers use correct methods for handling missing data or reported missing data methodology which may lead to reporting biased findings.
尽管系统评价已经探讨了倾向评分方法的各个方面是如何使用的，但尚无研究对如何用这些方法应对数据缺失的挑战进行综述。因此，本综述描述了在观察性比较效果研究中如何用倾向评分方法处理数据缺失问题。
从MEDLINE和EMBASE数据库中提取已发表的关于观察性比较效果研究的文章。
我们的检索得到167篇符合条件的文章。这些研究中的大多数（114篇；68%）进行了完整病例分析，其中只有53篇在方法部分说明了这一点。只有16篇文章报告使用了多重填补法。
很少有研究人员使用正确的方法处理数据缺失问题，或者报告数据缺失的方法，这可能导致研究结果报告存在偏差。
