• Kazuyoshi Aoyama, Ruxandra Pinto, Joel G Ray, Andrea Hill, Damon C Scales, and Robert A Fowler.
• From the Department of Anesthesia and Pain Medicine, Hospital for Sick Children, Toronto, Ontario, Canada (K.A.) the Program in Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada (K.A.) the Department of Critical Care Medicine (R.P., A.H., D.C.S., R.A.F.) the Sunnybrook Research Institute (K.A., R.P., A.H., D.C.S., R.A.F.), Sunnybrook Health Science Center, Toronto, Ontario, Canada the Keenan Research Centre of the Li Ka Shing Knowledge Institute (J.G.R.) the Department of Obstetrics and Gynecology, St. Michael's Hospital, Toronto, Ontario, Canada (J.G.R.) the Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada (K.A., J.G.R., D.C.S., R.A.F.).
• Anesthesiology. 2020 Sep 1; 133 (3): 500509500-509.

AbstractThere 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.

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