Adding propensity scores to pure prediction models fails to improve predictive performance.

ABSTRACT: Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration. Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and p

SUBMITTER: Nowacki AS 

PROVIDER: S-EPMC3740143 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature