|
Activity Number:
|
417
- Recent advancement on life time data analysis
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Lifetime Data Science Section
|
|
Abstract #317727
|
|
|
Title:
|
Semiparametrically Efficient and Doubly Robust Estimation of Hazard Difference for Competing Risks Data
|
|
Author(s):
|
Denise Rava* and Ronghui Xu
|
|
Companies:
|
Ucsd and University of California at San Diego
|
|
Keywords:
|
doubly robust;
semiparametric efficient;
hazards difference;
competing risk
|
|
Abstract:
|
We consider semiparametrically efficient and doubly robust estimation of hazard difference for competing risks data in observational studies. While the treatment effect of interest is described as a constant difference between the hazard functions of the two potential outcomes, we do not assume the additive hazards model to hold in order to adjust for the confounders. We derive the efficient score for the treatment effect using modern semiparametric theory and we use it to derive two double robust estimators with respect to the propensity score and the outcome model. We derive the asymptotic distributions of the estimators when the two working models are both correct, or when only one of them is correct. The estimators are applied to the data from a cohort of Japanese men in Hawaii followed since 1960s in order to study the effect of midlife drinking behavior on late life cognitive outcomes.
|
Authors who are presenting talks have a * after their name.
