Invited commentary: advancing propensity score methods in epidemiology.
Every epidemiologist knows that unmeasured confounding is a serious analytic problem, but practically speaking, there seems to be little one can do about it. In this issue of the Journal, Stürmer et al. (Am J Epidemiol 2007:165:1110-18) offer a novel solution that combines propensity score matching methods with measurement error regression models. They call this technique "propensity score calibration" (PSC) and assess its strengths and limitations with simulated data. Their analyses demonstrate that PSC greatly improves inference when the critical assumption of surrogacy holds, but when surrogacy does not hold, PSC estimation can exacerbate bias relative to uncorrected propensity score models. The benefits of propensity score methods (and PSC) lie not only with potentially improved effect estimation but with conceptualization and practice as well.
Oakes
J Michael
JM
Division of Epidemiology and Community Health, Minnesota Population Center, University of Minnesota, Minneapolis, MN 55454, USA. oakes@epi.umn.edu
Church
Timothy R
TR
D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D004812:Epidemiologic Methods; D016017:Odds Ratio