Having created a tentative matched design for an observational study, diagnostic checks are performed to see whether observed covariates exhibit reasonable balance, or alternatively whether further effort is required to improve the match. We illustrate the use of the cross-match test as an aid to appraising balance on high-dimensional covariates, and we discuss its close logical connections to ...

Randomized trials remain the most accepted design for estimating the effects of interventions, but they do not necessarily answer a question of primary interest: Will the program be effective in a target population in which it may be implemented? In other words, are the results generalizable? There has been very little statistical research on how to assess the generalizability, or "external val...

A two-step Bayesian propensity score approach is introduced that incorporates prior information in the propensity score equation and outcome equation without the problems associated with simultaneous Bayesian propensity score approaches. The corresponding variance estimators are also provided. The two-step Bayesian propensity score is provided for three methods of implementation: propensity sco...

A propensity score is the probability that a participant is assigned to the treatment group based on a set of baseline covariates. Propensity scores provide an excellent basis for equating treatment groups on a large set of covariates when randomization is not possible. This article provides a nontechnical introduction to propensity scores for clinical researchers. If all important covariates a...

This study investigated how bystanders, who have and have not been bullied, perceive their social and emotional maladjustment depending on the form of bullying (physical or verbal) they witness. Using propensity score matching, equivalent groups of 270 victimized and 270 non-victimized bystander groups were created based on middle school students' responses on the Bully Survey-Student Version (...

The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Wit...

This study evaluates the long-run effects of Finnish active labour market programmes in youth labour markets. The effectiveness of programmes is measured by a number of outcomes, including employment, unemployment, programme participation, education, being out-of-labour force and annual earnings. A nonparametric propensity score matching approach adapted for the case of multiple programmes is a...

In observational studies, investigators have no control over the treatment assignment. The treated and non-treated (that is, control) groups may have large differences on their observed covariates, and these differences can lead to biased estimates of treatment effects. Even traditional covariance analysis adjustments may be inadequate to eliminate this bias. The propensity score, defined as th...