TY - JOUR
T1 - Propensity score methods
T2 - Theory and practice for anesthesia research
AU - Schulte, Phillip J.
AU - Mascha, Edward J.
N1 - Publisher Copyright:
Copyright © 2018 International Anesthesia Research Society.
PY - 2018
Y1 - 2018
N2 - Observational data are often readily available or less costly to obtain than conducting a randomized controlled trial. With observational data, investigators may statistically evaluate the relationship between a treatment or therapy and outcomes. However, inherent in observational data is the potential for confounding arising from the nonrandom assignment of treatment. In this statistical grand rounds, we describe the use of propensity score methods (ie, using the probability of receiving treatment given covariates) to reduce bias due to measured confounders in anesthesia and perioperative medicine research. We provide a description of the theory and background appropriate for the anesthesia researcher and describe statistical assumptions that should be assessed in the course of a research study using the propensity score. We further describe 2 propensity score methods for evaluating the association of treatment or therapy with outcomes, propensity score matching and inverse probability of treatment weighting, and compare to covariate-adjusted regression analysis. We distinguish several estimators of treatment effect available with propensity score methods, including the average treatment effect, the average treatment effect for the treated, and average treatment effect for the controls or untreated, and compare to the conditional treatment effect in covariate-adjusted regression. We highlight the relative advantages of the various methods and estimators, describe analysis assumptions and how to critically evaluate them, and demonstrate methods in an analysis of thoracic epidural analgesia and new-onset atrial arrhythmias after pulmonary resection.
AB - Observational data are often readily available or less costly to obtain than conducting a randomized controlled trial. With observational data, investigators may statistically evaluate the relationship between a treatment or therapy and outcomes. However, inherent in observational data is the potential for confounding arising from the nonrandom assignment of treatment. In this statistical grand rounds, we describe the use of propensity score methods (ie, using the probability of receiving treatment given covariates) to reduce bias due to measured confounders in anesthesia and perioperative medicine research. We provide a description of the theory and background appropriate for the anesthesia researcher and describe statistical assumptions that should be assessed in the course of a research study using the propensity score. We further describe 2 propensity score methods for evaluating the association of treatment or therapy with outcomes, propensity score matching and inverse probability of treatment weighting, and compare to covariate-adjusted regression analysis. We distinguish several estimators of treatment effect available with propensity score methods, including the average treatment effect, the average treatment effect for the treated, and average treatment effect for the controls or untreated, and compare to the conditional treatment effect in covariate-adjusted regression. We highlight the relative advantages of the various methods and estimators, describe analysis assumptions and how to critically evaluate them, and demonstrate methods in an analysis of thoracic epidural analgesia and new-onset atrial arrhythmias after pulmonary resection.
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U2 - 10.1213/ANE.0000000000002920
DO - 10.1213/ANE.0000000000002920
M3 - Article
C2 - 29750691
AN - SCOPUS:85064112396
SN - 0003-2999
VL - 127
SP - 1074
EP - 1084
JO - Anesthesia and analgesia
JF - Anesthesia and analgesia
IS - 4
ER -