In health economic studies that use observational data, a key concern is how to adjust for imbalances in baseline covariates due to the non-random assignment of the programs under evaluation. Traditional methods of covariate adjustment such as regression, depend on correct model specification. Alternatives such as propensity score matching depend on covariate balance being achieved. We demonstr...

Propensity score methods are increasingly being used to reduce or minimize the effects of confounding when estimating the effects of treatments, exposures, or interventions when using observational or non-randomized data. Under the assumption of no unmeasured confounders, previous research has shown that propensity score methods allow for unbiased estimation of linear treatment effects (e.g., d...

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...

We compare the rationality of choice under risk – utility maximization, stochastic dominance, and expected-utility maximization – of students from one of the best universities in the US and one of the best universities in Africa. The US subjects came nearer to consistency with utility maximization and the dominance principle, but there are no differences between the two samples in consistency w...

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...