Efficient Estimation of Semiparametric Transformation Models for the Cumulative Incidence of Competing Risks

DOI10.1111/RSSB.12177zbMath1414.62407arXiv1602.01787OpenAlexW2340690559WikidataQ47958683 ScholiaQ47958683MaRDI QIDQ5381092

Lu Mao, D. Y. Lin

Publication date: 7 June 2019

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1602.01787

zbMATH Keywords

survival analysisproportional hazardscensoringprofile likelihoodsemiparametric efficiencynon-parametric maximum likelihood estimation

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Censored data models (62N01)

Related Items (5)

Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design ⋮ Nonparametric estimation for competing risks survival data subject to left truncation and interval censoring ⋮ Semiparametric Regression Analysis of Interval-Censored Competing Risks Data ⋮ Efficient Estimation in the Fine and Gray Model ⋮ Impact of covariate omission and categorization from the Fine-Gray model in randomized-controlled trials

This page was built for publication: Efficient Estimation of Semiparametric Transformation Models for the Cumulative Incidence of Competing Risks