There are an increasing number of “big data” studies in anesthesia that seek to answer clinical questions by observing the care and outcomes of many patients across a variety of care settings. This Readers’ Toolbox will explain how to estimate the influence of patient factors on clinical outcome, addressing bias and confounding. One approach to limit the influence of confounding is to perform a clinical trial. When such a trial is infeasible, observational studies using robust regression techniques may be able to advance knowledge. Logistic regression is used when the outcome is binary (e.g., intracranial hemorrhage: yes or no), by modeling the natural log for the odds of an outcome. Because outcomes are influenced by many factors, we commonly use multivariable logistic regression to estimate the unique influence of each factor. From this tutorial, one should acquire a clearer understanding of how to perform and assess multivariable logistic regression.
Determining Associations and Estimating Effects with Regression Models in Clinical Anesthesia
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Submitted for publication July 12, 2018. Accepted for publication May 19, 2020. Published online first on July 1, 2020.
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Kazuyoshi Aoyama, Ruxandra Pinto, Joel G. Ray, Andrea Hill, Damon C. Scales, Robert A. Fowler; Determining Associations and Estimating Effects with Regression Models in Clinical Anesthesia. Anesthesiology 2020; 133:500–509 doi: https://doi.org/10.1097/ALN.0000000000003425
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