We have located links that may give you full text access.
Determining Associations and Estimating Effects with Regression Models in Clinical Anesthesia.
Anesthesiology 2020 September 2
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.
Full text links
Related Resources
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app