Propensity scores in cardiovascular research.

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 -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 and thus reduce this bias. In a randomized experiment, the randomization of participants to different treatments minimizes the chance of differences on observed or unobserved covariates. However, in nonrandomized studies, systematic differences can exist between treatment groups. To control for this potential bias, information on measured covariates can be incorporated into the study design (eg, through matched sampling) or into estimation of the treatment effect (eg, through stratification or covariance adjustment). However, such methods of adjustment can often use only a limited number of covariates, whereas adjustments that use propensity scores do not have this limitation. A simple illustration of how an imbalance on covariates could influence a treatment effect estimate is as follows. Consider a nonrandomized study with 2 groups and a binary outcome with the data shown in the Table. We can clearly see in the Table that gender is not balanced between the 2 groups (80% males in Group A versus 10% in Group B). The apparent treatment difference between groups would not be significant if we adjusted for gender. In other words, if we created balance between the 2 groups on the basis of gender, we would recognize that the apparent treatment effect is caused by gender and not group. In most observational studies, there may be several variables that are imbalanced at the same time between groups and the propensity score methodology allows one to simultaneously balance all the covariates and make more valid inferences about treatment effects.