Estimation of conditional cumulative incidence functions under generalized semiparametric regression models with missing covariates, with application to analysis of biomarker correlates in vaccine trials
[1];
[2];
- Univ. of North Carolina, Charlotte, NC (United States)
- Univ. of North Florida, Jacksonville, FL (United States)
- U.S. Food and Drug Administration (FDA), Silver Spring, MD (United States). Center for Biologics Evaluation and Research (CBER)
- Univ. of Washington, Seattle, WA (United States); Fred Hutchinson Cancer Research Center, Seattle, WA (United States)
Herein, 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.
- Research Organization:
- Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States)
- Sponsoring Organization:
- Military HIV Research Program; National Cancer Institute (NCI); National Institute of Allergy and Infectious Diseases (NIAID); National Science Foundation (NSF); USDOE Office of Science (SC)
- Grant/Contract Number:
- SC0014664
- OSTI ID:
- 1981395
- Journal Information:
- Canadian Journal of Statistics, Journal Name: Canadian Journal of Statistics Journal Issue: 1 Vol. 51; ISSN 0319-5724
- Publisher:
- Wiley; Statistical Society of CanadaCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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