A propensity score is the probability that an individual will be assigned to a condition or group, given a set of baseline covariates when the assignment is made. For example, the type of drug treatment given to a patient in a real-world setting might be non-randomly based on the patient's age, gender, geographic location, and socioeconomic status when the drug is prescribed. Propensity scores ...

Propensity-score methods are increasingly being used to reduce the impact of treatment-selection bias in the estimation of treatment effects using observational data. Commonly used propensity-score methods include covariate adjustment using the propensity score, stratification on the propensity score, and propensity-score matching. Empirical and theoretical research has demonstrated that matchi...

Propensity score matching is a method to reduce bias in non-randomized and observational studies. Propensity score matching is mainly applied to two treatment groups rather than multiple treatment groups, because some key issues affecting its application to multiple treatment groups remain unsolved, such as the matching distance, the assessment of balance in baseline variables, and the choice o...

BACKGROUND In observational research, propensity score techniques can be used to account for baseline differences between compared therapies. Although propensity scores are used increasingly often, their limitations in settings without complete data may not be recognized. OBJECTIVES We sought to evaluate the ability of propensity score matching to mitigate confounding by indication in an obse...

1. Introduction Logistic propensity models for nonresponse adjustments have been used for various studies (Little 1986). The logistic models used to compute the scores reflect the propensity to respond based on attributes of both respondents and nonrespondents. Propensity scores can be used to compute explicit adjustments factors or to form weighting cells. Recent work has shown the benefits of...

Propensity score matching estimators (Rosenbaum and Rubin, 1983) are widely used in evaluation research to estimate average treatment effects. In this article, we derive the large sample distribution of propensity score matching estimators. Our derivations take into account that the propensity score is itself estimated in a first step, prior to matching. We prove that first step estimation of t...