• Alex Illescas, Haoyan Zhong, Crispiana Cozowicz, Gonzalez Della ValleAlejandroADepartment of Orthopedic Surgery, Hospital for Special Surgery, New York, New York., Jiabin Liu, Stavros G Memtsoudis, and Jashvant Poeran.
• From the Department of Anesthesiology, Critical Care & Pain Management, Hospital for Special Surgery, New York, New York.
• Anesth. Analg. 2022 Mar 1; 134 (3): 540-547.

AbstractThe use of large data sources such as registries and claims-based data sets to perform health services research in anesthesia has increased considerably, ultimately informing clinical decisions, supporting evaluation of policy or intervention changes, and guiding further research. These observational data sources come with limitations that must be addressed to effectively examine all aspects of health care services and generate new individual- and population-level knowledge. Several statistical methods are growing in popularity to address these limitations, with the goal of mitigating confounding and other biases. In this article, we provide a brief overview of common statistical methods used in health services research when using observational data sources, guidance on their interpretation, and examples of how they have been applied to anesthesia-related health services research. Methods described involve regression, propensity scoring, instrumental variables, difference-in-differences, interrupted time series, and machine learning.Copyright © 2022 International Anesthesia Research Society.

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