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Determining Associations and Estimating Effects with Regression Models in Clinical Anesthesia
Kazuyoshi Aoyama1, Ruxandra Pinto, Joel G Ray
1From 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.).
Big data studies in anesthesia use patient data to answer clinical questions. This tutorial explains how to use multivariable logistic regression to assess patient factors influencing outcomes, addressing bias and confounding in observational studies.
Area of Science:
- Anesthesiology
- Biostatistics
- Clinical Research Methodology
Background:
- Big data research in anesthesia is growing, necessitating methods to analyze large patient datasets.
- Observational studies are crucial for clinical questions when trials are infeasible.
- Understanding patient factors influencing outcomes is key to improving care.
Observation:
- This tutorial focuses on using robust regression techniques, specifically multivariable logistic regression.
- Logistic regression models binary outcomes (e.g., intracranial hemorrhage) by analyzing the log odds.
- Multivariable logistic regression estimates the unique influence of individual factors on outcomes.
Findings:
- The tutorial aims to clarify the application and assessment of multivariable logistic regression.
- It addresses methods to mitigate bias and confounding in observational anesthesia studies.
- Readers will gain a clearer understanding of how to perform and interpret these analyses.
Implications:
- Improved understanding of logistic regression can enhance the analysis of big data in anesthesia.
- Accurate assessment of patient factors can lead to more targeted clinical interventions.
- This knowledge supports evidence-based practice by leveraging observational study findings.
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