Estimation of conditional cumulative incidence functions under generalized semiparametric regression models with missing covariates, with application to analysis of biomarker correlates in vaccine trials
Abstract
This article presents generalized semiparametric regression models for conditional cumulative incidence functions with competing risks data when covariates are missing by sampling design or happenstance. A doubly robust augmented inverse probability weighted (AIPW) complete‐case approach to estimation and inference is investigated. This approach modifies IPW complete‐case estimating equations by exploiting the key features in the relationship between the missing covariates and the phase‐one data to improve efficiency. An iterative numerical procedure is derived to solve the nonlinear estimating equations. The asymptotic properties of the proposed estimators are established. A simulation study examining the finite‐sample performances of the proposed estimators shows that the AIPW estimators are more efficient than the IPW estimators. The developed method is applied to the RV144 HIV‐1 vaccine efficacy trial to investigate vaccine‐induced IgG binding antibodies to HIV‐1 as correlates of acquisition of HIV‐1 infection while taking account of whether the HIV‐1 sequences are near or far from the HIV‐1 sequences represented in the vaccine construct.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 24, 2022
- Source ID
- 10.1002/cjs.11693
Entities
People
- Fei Heng
- Peter B Gilbert
- Unkyung Lee
- Yanqing Sun
Organizations
- Center for Biologics Evaluation and Research
- Fred Hutchinson Cancer Center
- National Cancer Institute
- National Institute of Allergy and Infectious Diseases
- National Science Foundation Division of Mathematical Sciences
- University of North Carolina at Charlotte
- University of North Florida
- University of Washington