Efficient semiparametric mixture inferences on cure rate models for competing risks

DOI10.1002/cjs.11256zbMath1321.62136OpenAlexW1931398059MaRDI QIDQ2949768

Janice N. Cormier, Xuelin Huang, Sangbum Choi

Publication date: 2 October 2015

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/cjs.11256

zbMATH Keywords

mixturemartingaletransformation modelcure modelsubgroup analysiscumulative incidencenonparametric likelihood

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Censored data models (62N01) Medical applications (general) (92C50)

Uses Software

• cmprsk

Cites Work

• Unnamed Item
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• Maximum likelihood estimation of semiparametric mixture component models for competing risks data
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• On semiparametric transformation cure models
• Weighted Breslow-type and maximum likelihood estimation in semiparametric transformation models
• Latent Variable Modelling: A Survey*
• Predicting cumulative incidence probability by direct binomial regression
• A Proportional Hazards Model for the Subdistribution of a Competing Risk
• A Nonparametric Mixture Model for Cure Rate Estimation
• Constrained parametric model for simultaneous inference of two cumulative incidence functions
• Semiparametric analysis of mixture regression models with competing risks data

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