title: Methods to adjust for bias and confounding in critical care health services research involving observational data.
abstract: 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.
authors.affiliation: Department of Anesthesiology, Columbia Presbyterian Medical Center, Columbia University, New York, NY 10032, USA. hannahwunsch@hotmail.com
authors.forename: Hannah
authors.initials: H
authors.lastname: Wunsch
authors.forename: Walter T
authors.initials: WT
authors.lastname: Linde-Zwirble
authors.forename: Derek C
authors.initials: DC
authors.lastname: Angus
mesh_terms: D015982:Bias; D015986:Confounding Factors, Epidemiologic; D003422:Critical Care; D006302:Health Services Research / Q000379:methods*; D006801:Humans; D019370:Observation; D016032:Randomized Controlled Trials as Topic; D012107:Research Design