Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.