Estimation of conditional cumulative incidence functions under generalized semiparametric regression models with missing covariates, with application to analysis of biomarker correlates in vaccine trials
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Publication:6059442
DOI10.1002/cjs.11693OpenAlexW4214509415MaRDI QIDQ6059442
Yanqing Sun, Peter B. Gilbert, Fei Heng, Unkyung Lee
Publication date: 2 November 2023
Published in: Canadian Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://www.osti.gov/biblio/1981395
two-phase samplingcompeting riskscumulative incidence functionaugmented inverse probability weighted complete-case estimationvaccine-induced antibody immune response biomarkers
Cites Work
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- Analysis of generalized semiparametric regression models for cumulative incidence functions with missing covariates
- Analysis of two-phase sampling data with semiparametric additive hazards models
- Semiparametric theory and missing data.
- Extensions and Applications of the Cox‐Aalen Survival Model
- Semiparametric estimators for the regression coefficients in the linear transformation competing risks model with missing cause of failure
- Predicting cumulative incidence probability by direct binomial regression
- A case-cohort design for epidemiologic cohort studies and disease prevention trials
- Inference and missing data
- Asymptotic Statistics
- Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
- A partly parametric additive risk model
- A Proportional Hazards Model for the Subdistribution of a Competing Risk
- Hypothesis tests for stratified mark‐specific proportional hazards models with missing covariates, with application to HIV vaccine efficacy trials
- A Generalization of Sampling Without Replacement From a Finite Universe
- Maximum Likelihood Estimation of Misspecified Models