Speaker
Dr. Yinghao Pan, University of North Carolina at Charlotte
Title
Improved doubly robust estimation in learning optimal individualized treatment rules
Physical Location
Webex: https://msstate.webex.com/msstate/j.php?MTID=mf30c658a783eca7d71ee8777480729a1
Abstract: Individualized treatment rules (ITRs) recommend treatment according to patient characteristics. There is a growing interest in developing novel and efficient statistical methods in constructing ITRs. We propose an improved doubly robust estimator of the optimal ITRs. The proposed estimator is based on a direct optimization of an augmented inverse-probability weighted estimator (AIPWE) of the expected clinical outcome over a class of ITRs. The method enjoys two key properties. First, it is doubly robust, meaning that the proposed estimator is consistent when either the propensity score or the outcome model is correct. Second, it achieves the smallest variance among the class of doubly robust estimators when the propensity score model is correctly specified, regardless of the specification of the outcome model. Simulation studies show that the estimated ITRs obtained from our method yield better results than those obtained from current popular methods. Data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study is analyzed as an illustrative example.
Webex information:
Link: https://msstate.webex.com/msstate/j.php?MTID=mf30c658a783eca7d71ee87774…
Meeting number (access code): 120 693 0183
Meeting password: scsMvybZ