Article
Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study.
Institute for Clinical Evaluative Sciences, Toronto, Ont., Canada.
Statistics in Medicine (Impact Factor: 2.04). 03/2007; 26(4):75468. DOI: 10.1002/sim.2618 Source: PubMed

Article: Balancing Treatment and Control Groups in Quasi‐Experiments: An Introduction to Propensity Scoring
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ABSTRACT: Organizational and applied sciences have long struggled with improving causal inference in quasi‐experiments. We introduce organizational researchers to propensity scoring, a statistical technique that has become popular in other applied sciences as a means for improving internal validity. Propensity scoring statistically models how individuals in a quasi‐experiment have been assigned to conditions in order to estimate treatment effects among individuals with approximately equal probabilities of receiving the treatment. If propensity scores are created from relevant covariates, matching on the propensity score makes treatment assignment ignorable and approximates a true experimental design. We illustrate how matching on the propensity score can be applied by using 2 examples: examining the effects of online instruction and estimating the benefits of preparatory coaching for the SAT. In both cases, propensity‐scoring methods effectively reduced inequivalence between treatment and control groups on many variables. Propensity scoring stands out as a valuable technique capable of improving causal inference from many of organizational research's quasi‐experiments.Personnel Psychology 06/2013; 66(2). · 2.93 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: The discovery and development of new antimicrobials is critically important especially as multidrug resistant bacteria continue to emerge. Little has been written about the epidemiological issues in nonrandomized trials aiming to evaluate the superiority of one antibiotic over another. In this manuscript, we outline some of the methodological difficulties in demonstrating superiority and discuss potential approaches to these problems. Many of the difficulties arise due to confounding by indication which we define and explain. Epidemiological methods including restriction, matching, stratification, multivariable regression, propensity scores and instrumental variables are discussed.Clinical Infectious Diseases 06/2014; · 9.42 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: Propensityscore matching is frequently used to estimate the effect of treatments, exposures, and interventions when using observational data. An important issue when using propensityscore matching is how to estimate the standard error of the estimated treatment effect. Accurate variance estimation permits construction of confidence intervals that have the advertised coverage rates and tests of statistical significance that have the correct type I error rates. There is disagreement in the literature as to how standard errors should be estimated. The bootstrap is a commonly used resampling method that permits estimation of the sampling variability of estimated parameters. Bootstrap methods are rarely used in conjunction with propensityscore matching. We propose two different bootstrap methods for use when using propensityscore matching without replacementand examined their performance with a series of Monte Carlo simulations. The first method involved drawing bootstrap samples from the matched pairs in the propensityscorematched sample. The second method involved drawing bootstrap samples from the original sample and estimating the propensity score separately in each bootstrap sample and creating a matched sample within each of these bootstrap samples. The former approach was found to result in estimates of the standard error that were closer to the empirical standard deviation of the sampling distribution of estimated effects. © 2014 The Authors Statistics in Medicine Published by John Wiley & Sons, Ltd.Statistics in Medicine 08/2014; · 2.04 Impact Factor
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