观察研究
医学
工具变量
结果(博弈论)
倾向得分匹配
随机化
干预(咨询)
选择偏差
危害
随机对照试验
计量经济学
统计
内科学
心理学
精神科
病理
数理经济学
社会心理学
经济
数学
作者
Thomas Agoritsas,Arnaud Merglen,Nilay D. Shah,Martin O’Donnell,Gordon Guyatt
出处
期刊:JAMA
[American Medical Association]
日期:2017-02-21
卷期号:317 (7): 748-748
被引量:121
标识
DOI:10.1001/jama.2016.20029
摘要
Observational studies almost always have bias because prognostic factors are unequally distributed between patients exposed or not exposed to an intervention. The standard approach to dealing with this problem is adjusted or stratified analysis. Its principle is to use measurement of risk factors to create prognostically homogeneous groups and to combine effect estimates across groups. The purpose of this Users’ Guide is to introduce readers to fundamental concepts underlying adjustment as a way of dealing with prognostic imbalance and to the basic principles and relative trustworthiness of various adjustment strategies. One alternative to the standard approach is propensity analysis, in which groups are matched according to the likelihood of membership in exposed or unexposed groups. Propensity methods can deal with multiple prognostic factors, even if there are relatively few patients having outcome events. However, propensity methods do not address other limitations of traditional adjustment: investigators may not have measured all relevant prognostic factors (or not accurately), and unknown factors may bias the results. A second approach, instrumental variable analysis, relies on identifying a variable associated with the likelihood of receiving the intervention but not associated with any prognostic factor or with the outcome (other than through the intervention); this could mimic randomization. However, as with assumptions of other adjustment approaches, it is never certain if an instrumental variable analysis eliminates bias. Although all these approaches can reduce the risk of bias in observational studies, none replace the balance of both known and unknown prognostic factors offered by randomization.
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