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(Circulation. 2007;115:2340-2343.)
© 2007 American Heart Association, Inc.

Statistical Primer for Cardiovascular Research

Propensity Scores in Cardiovascular Research

Ralph B. D’Agostino, Jr, PhD

From the Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC.

Correspondence to Dr Ralph B. D’Agostino, Jr, Department of Biostatistical Sciences, Medical Center Boulevard, Wake Forest University School of Medicine, Winston-Salem, NC 27157. E-mail rdagosti@wfubmc.edu

Key Words: cardiovascular diseases • epidemiology • risk factors • statistics

An extract of the first 250 words of the full text is provided, because this article has no abstract.

	Introduction

 
Propensity scores have been used to reduce bias in observational studies in many fields and are becoming more widely used in cardiovascular research.¹ The goal of this statistical primer is to present the definition of propensity scores and to illustrate their use by describing some recent examples found in the cardiovascular disease research literature.

Large-scale epidemiological cohort studies such as the Multi-Ethnic Study of Atherosclerosis (MESA)² are designed to follow a large sample of participants over time without active administration of any interventions. Within MESA, lack of randomization can complicate potential treatment comparisons such as the impact of ß-blocker versus angiotensin-converting enzyme inhibitor usage. Nonrandomized comparisons may also arise from within a randomized clinical trial. For instance, the Clopidogrel as Adjunctive Reperfusion Therapy - Thrombolysis in Myocardial Infarction 28 (CLARITY-TIMI 28) trial³ is a randomized study that compares clopidogrel with placebo in 3491 ST-elevation myocardial infarction patients aged 18 to 75 years who have undergone fibrinolysis. In addition to the primary end points, investigators wished to compare the effects of low molecular weight heparin with unfractionated heparin on angiographic and clinical outcomes in participants.⁴ These treatments were not randomly assigned.

In studies such as these, the treatment groups may markedly differ with respect to the observed pretreatment covariates measured on participants. These differences could lead to biased estimates of treatment effects. The propensity score for an individual, defined as the conditional probability of being treated given the individual’s covariates, can be used to balance the covariates in the 2 groups . . . [Full Text of this Article]

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