Methods in Cardiovascular ResearchA Review of Propensity-Score Methods and Their Use in Cardiovascular Research
Section snippets
Definition and Fundamentals of the Propensity Score
Rosenbaum and Rubin defined the propensity score as the probability (from 0-1) of treatment assignment based on observed baseline covariates.5 Thus, it is the conditional probability that a participant will be assigned to the treatment group based on patient demographics and comorbidities that are measured at the time of group assignment.2, 4 The propensity score can be thought of as a balancing score that, like random assignment, attempts to balance the distribution of these measured
Assessing for Model Adequacy
The success of propensity-score modelling can be evaluated by comparing the balance between treated and control participants after accounting for the propensity score; if there are imbalances, especially for prognostically important covariates, the model has been inadequately specified.13 Once a propensity-score model has been created, appropriate methods should be used to examine whether the model does indeed lead to similar distribution of covariates (ie, correctly specified) between treated
Propensity-Score Matching
Propensity-score matching is 1 of the most common ways to use the propensity score. It is a widely used method in the cardiovascular literature, although its implementation and reporting have been found to be poor.11 In propensity-score matching, participants with similar propensity scores in the treatment and control groups are matched.
An example of this technique was demonstrated in a study by Tu et al.8 comparing the effect of drug-eluting stents vs bare-metal stents on proportions of
Stratification
Stratification is another commonly used method to adjust for systematic differences between the treatment and control groups in a study.4 An example of this is found in an observational study published by Thuny et al.,9 comparing whether the timing of surgery (≤ 1 week vs > 1 week) after the beginning of antibiotic therapy influences mortality and morbidity in adults with complicated infective endocarditis. To address differences in baseline characteristics, propensity scores were generated for
Covariate Adjustment
The propensity score can also be used in a regression (covariance) adjustment.2 An example of this is a study investigating whether metabolic syndrome (MetS) was associated with late mortality in patients undergoing coronary artery bypass grafting (CABG).28 To adjust for differences in baseline covariates, the authors used propensity-score modelling, with the dependent variable being MetS and multiple covariates including sex and diabetes. The investigators elected to estimate adjusted hazard
Inverse Probability of Treatment Weighting
Another means of using the propensity score is to create a weight based on the score itself. An example of this was demonstrated in a recent study of nearly 190,000 patients by Weintraub et al.30 comparing percutaneous coronary intervention (PCI) with CABG using the American College of Cardiology Foundation and the Society of Thoracic Surgeons databases. As expected, many of the key baseline covariates were significantly different between the 2 groups; the authors addressed this by using
Discussion
Observational studies of treatment effects have inherent limitations, including potential imbalances of known and unknown confounders between treatment and control groups. The propensity score is a balancing score that makes the distribution of measured covariates similar between the treatment and control groups.4 Once propensity scores are estimated and 1 of the methods described here is applied, appropriate balance diagnostics should be performed. Presence of imbalances mean that the
Conclusions
In summary, using propensity scores is a good technique in observational studies to help achieve a better balance between the treatment and control groups. Of the 4 methods using propensity scores, matching and IPTW seem to perform better in reducing bias than do stratification and covariate adjustment. The matching technique would be ideal in an observational study in which there are more patients in the control group than in the treatment group and the resulting match is well balanced; in
Funding Sources
S.D. is supported by the Vanier Canada Graduate Scholarship, Canadian Institutes of Health Research. P.C.A. is supported by a Career Investigator award from the Heart & Stroke Foundation of Canada. J.V.T. is supported by a Canada Research Chair in Health Services Research and a Career Investigator Award from the Heart and Stroke Foundation of Canada. D.T.K. is supported by a Clinician Investigator Award from the Heart and Stroke Foundation of Canada. C.D.M. is supported by a merit award from
Disclosures
The authors have no conflicts of interest to disclose.
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