A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable…

By T. Stürmer, Manisha Joshi, Robert J. Glynn, J. Avorn, Kenneth J. Rothman, Sebastian Schneeweiss. Journal of Clinical Epidemiology, 2006

722 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a611feb335b71d6bd1f7134503c39de7c6c3732c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Statistics and Probability

Citations in the corpus

A critical appraisal of propensity‐score matching in the medical literature between 1996 and 2003 (2008, background): “A recent systematic review examined the use propensity methods in the clinical literature [28].”
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies (2011, methodology): “Finally, a recent review of propensity score methods (Stürmer et al., 2006) documented that many authors report the c-statistic of the propensity score model.”
Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples (2009, methodology): “Reviews of the use of propensity-score methods in the medical literature have found that they are most frequently used to estimate the effect of treatment on binary or time-to-event outcomes, and are rarely used for continuous outcomes [6].”
Variable selection for propensity score models. (2006, methodology): “Furthermore, in many PS analyses, investigators report the c statistic (the area under the receiver operating characteristic curve) for the final PS model as a means of assessing the model’s adequacy (7, 8 ). Implicit in this practice is the assumption that PS models that are better predictors or d”
High-dimensional propensity score adjustment in studies of treatment effects using health care claims data (2009, methodology): “As the output of Step 6 includes each subject’s propensity score, other ways to use propensity scores in the outcome estimation may be applied, including matching, inverse probability of treatment weighting, or modeling the propensity score as continuous variable.(24) The high dimensional propensity”
Assessing the Effectiveness of a College Freshman Seminar Using Propensity Score Adjustments (2011, background): “Given the plethora of literature concerning retention (Astin 1993; Aitken 1982; Cabrera et al. 1993; Engberg and Mayhew 2007; Grayson 1988; Hunter 2006; Strauss and Volkwein 2004; Tinto 1993, 1998), it is clear that universities have been battling student attrition for many years.”
Meta-analysis of the impact of 9 medication classes on falls in elderly persons. (2009, background): “Propensity scores can estimate the probability of exposure and permit the matching of individuals across groups with similar propensity scores or probabilities of exposure and are thought to be most appropriate when the treatment is frequent, with rare outcomes.(46,47) Multivariable modeling incorpo”
A comparison of 12 algorithms for matching on the propensity score (2013, methodology): “Second, in a systematic review of the use of propensity score methods in the medical literature, we observed that these methods have been used in datasets of size less than 1000 [16].”
Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies (2010, neutral): “Each subject in the sample has a pair of potential outcomes: Yi(0) and Yi(1), the outcome under the control treatment and the outcome under the active treatment, respectively.”