Readers' Toolbox  |   September 2020
Determining Associations and Estimating Effects with Regression Models in Clinical Anesthesia
Author Notes
  • 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.) and 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.) and the Department of Obstetrics and Gynecology, St. Michael’s Hospital, Toronto, Ontario, Canada (J.G.R.); and 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.).
  • Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are available in both the HTML and PDF versions of this article. Links to the digital files are provided in the HTML text of this article on the Journal’s Web site (www.anesthesiology.org).
    Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are available in both the HTML and PDF versions of this article. Links to the digital files are provided in the HTML text of this article on the Journal’s Web site (www.anesthesiology.org).×
  • Submitted for publication July 12, 2018. Accepted for publication May 19, 2020. Published online first on July 1, 2020.
    Submitted for publication July 12, 2018. Accepted for publication May 19, 2020. Published online first on July 1, 2020.×
  • Address correspondence to Dr. Aoyama: 555 University Avenue, #2211, Toronto, Ontario M5G 1X8, Canada. kazu.aoyama@utoronto.ca. Anesthesiology’s articles are made freely accessible to all readers on www.anesthesiology.org, for personal use only, 6 months from the cover date of the issue.
Article Information
Cardiovascular Anesthesia / Central and Peripheral Nervous Systems / Obstetric Anesthesia / Quality Improvement / Readers' Toolbox
Readers' Toolbox   |   September 2020
Determining Associations and Estimating Effects with Regression Models in Clinical Anesthesia
Anesthesiology 9 2020, Vol.133, 500-509. doi:https://doi.org/10.1097/ALN.0000000000003425
Anesthesiology 9 2020, Vol.133, 500-509. doi:https://doi.org/10.1097/ALN.0000000000003425
Abstract

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.