Analysis of generalized semiparametric regression models for cumulative incidence functions with missing covariates

DOI10.1016/j.csda.2018.01.003zbMath1469.62096OpenAlexW2787915240WikidataQ89070557 ScholiaQ89070557MaRDI QIDQ1662316

Peter B. Gilbert, Thomas H. Scheike, Unkyung Lee, Yanqing Sun

Publication date: 17 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc5993453

zbMATH Keywords

time-varying effectstwo-phase samplingcompeting riskssemiparametric regression modelinverse probability weighted complete-caseRV144 vaccine efficacy trial

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Estimation in survival analysis and censored data (62N02)

Related Items (2)

Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design ⋮ Estimation of conditional cumulative incidence functions under generalized semiparametric regression models with missing covariates, with application to analysis of biomarker correlates in vaccine trials

Uses Software

• cmprsk
• osDesign

Cites Work

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