Variable Selection for Propensity Score Models
Harvard University · Brigham and Women's Hospital
Abstract
Despite the growing popularity of propensity score (PS) methods in epidemiology, relatively little has been written in the epidemiologic literature about the problem of variable selection for PS models. The authors present the results of two simulation studies designed to help epidemiologists gain insight into the variable selection problem in a PS analysis. The simulation studies illustrate how the choice of variables that are included in a PS model can affect the bias, variance, and mean squared error of an estimated exposure effect. The results suggest that variables that are unrelated to the exposure but related to the outcome should always be included in a PS model. The inclusion of these variables will…
Citation impact
- FWCI
- 26.84
- Percentile
- 100%
- References
- 22
Authors
6- MAM. Alan BrookhartCorresponding
Harvard University, Brigham and Women's Hospital
- SSSebastian Schneeweiß
Brigham and Women's Hospital, Harvard University
- KJKenneth J. Rothman
Harvard University, Brigham and Women's Hospital
- RJRobert J. Glynn
Brigham and Women's Hospital, Harvard University
- JAJerry Avorn
Harvard University, Brigham and Women's Hospital
Topics & keywords
- Confounding
- Statistics
- Variance (accounting)
- Selection bias
- Propensity score matching
- Econometrics
- Contrast (vision)
- Mean squared error
- Good health and well-being