DMS Colloquium: Dr. Youngjoo Cho

Speaker: Dr. Youngjoo Cho, Zilber School of Public Health, University of Wisconsin-Milwaukee

Title: Covariate Adjustment for Treatment Effect On Competing Risks Data in Randomized Clinical Trials

Abstract : The double blinded randomization trial is a gold standard for estimating average causal effect (ACE). It does not require adjustment for covariates. However, in most case, adjustment of covariates that are strong predictor of the outcome could improve efficiency for the estimation of ACE. But when covariates are high-dimension, adjust all covariates in the model will lose efficiency or worse, lose identifiability. Recent work has shown that for linear regression, an estimator under risk consistency (e.g., LASSO, Random Forest) for the regression coefficients could always lead to improvement in efficiency. In this work, we studied the behavior of adjustment estimator for competing risk data analysis. Simulation study shows that the covariate adjustment provides the more efficient estimator than unadjusted one.