What Are Propensity Scores?
I have been meaning to write about propensity scores for some time now. As a pulmonary and critical care physician in the United States, I dutifully read my Chest journal whenever possible. The editor sends emails to members of the Chest College that highlight important articles as they are published online.
This past week, two of the highlighted studies used propensity scores to analyze their data. The next email I received was from the pulmonary medicine fellowship director at my hospital. He had attached the article we will be discussing in our journal club this week. That's right, another study using a propensity score. I figured it was time to talk more about this technique.
Propensity scores are used to reduce confounding in observational studies. When measuring the effect of an intervention using observational data, researchers need to "match" the patients with a population of control individuals who did not receive the intervention. Although it's possible to match for specific covariates (eg, age, race, and sex), two problems will inevitably arise: (1) If the controls are sampled from a comparable group of people who did not receive the intervention, there is a selection bias (unmeasured factor or factors that might cause clinicians to withhold the intervention and that can systemically influence group differences); and (2) depending on the size of the population being sampled, matching each patient to another using specific covariates may reduce the sample size. Only randomization can truly eliminate selection bias, but propensity matching offers a popular solution to both problems.