Wunsch Hannah, Linde-Zwirble Walter T, Angus Derek C
Department of Anesthesiology, Columbia Presbyterian Medical Center, Columbia University, New York, NY 10032, USA.
J Crit Care. 2006 Mar;21(1):1-7. doi: 10.1016/j.jcrc.2006.01.004.
Observational data are often used for research in critical care. Unlike randomized controlled trials, where randomization theoretically balances confounding factors, studies involving observational data pose the challenge of how to adjust appropriately for the bias and confounding that are inherent when comparing two or more groups of patients. This paper first highlights the potential sources of bias and confounding in critical care research and then reviews the statistical techniques available (matching, stratification, multivariable adjustment, propensity scores, and instrumental variables) to adjust for confounders. Finally, issues that need to be addressed when interpreting the results of observational studies, such as residual confounding, causality, and missing data, are discussed.
观察性数据常用于重症监护研究。与随机对照试验不同，在随机对照试验中，随机化理论上可平衡混杂因素，而涉及观察性数据的研究面临如何适当调整两组或多组患者比较时固有的偏差和混杂问题的挑战。本文首先强调重症监护研究中偏差和混杂的潜在来源，然后回顾可用于调整混杂因素的统计技术（匹配、分层、多变量调整、倾向得分和工具变量）。最后，讨论解释观察性研究结果时需要解决的问题，如残余混杂、因果关系和缺失数据。
