Marginal semiparametric transformation models for clustered multivariate competing risks data
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Publication:6629377
DOI10.1002/sim.9573zbMATH Open1547.62265MaRDI QIDQ6629377
Lu Mao, Kwang Woo Ahn, Soyoung Kim, Yizeng He
Publication date: 29 October 2024
Published in: Statistics in Medicine (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Longitudinal data analysis using generalized linear models
- Competing risks regression for clustered data
- Score test of homogeneity for survival data. (With discussion)
- Estimation in the positive stable shared frailty Cox proportional hazards model
- A Proportional Hazards Regression Model for the Subdistribution with Covariates-adjusted Censoring Weight for Competing Risks Data
- Marginal Models for Clustered Time-to-Event Data with Competing Risks Using Pseudovalues
- Models for Longitudinal Data: A Generalized Estimating Equation Approach
- Predicting cumulative incidence probability by direct binomial regression
- The Analysis of Failure Times in the Presence of Competing Risks
- Marginal Regression Models for Multivariate Failure Time Data
- A Proportional Hazards Model for the Subdistribution of a Competing Risk
- Maximum Likelihood Estimation in Semiparametric Regression Models with Censored Data
- Weighted NPMLE for the Subdistribution of a Competing Risk
- A semiparametric random effects model for multivariate competing risks data
- Efficient Estimation of Semiparametric Transformation Models for the Cumulative Incidence of Competing Risks
- Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function
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