A Semiparametric Mixture Model for Analyzing Clustered Competing Risks Data

DOI10.1111/j.1541-0420.2005.00341.xzbMath1079.62117OpenAlexW1971377986WikidataQ31002475 ScholiaQ31002475MaRDI QIDQ5717154

Kalyan Das, Malay Naskar, Joseph G. Ibrahim

Publication date: 12 January 2006

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2005.00341.x

zbMATH Keywords

frailty modelMonte Carlo ECM algorithmSenegal AIDS dataweighted Chinese restaurant algorithm

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Censored data models (62N01) Estimation in survival analysis and censored data (62N02)

Related Items

A dependent Dirichlet process model for survival data with competing risks, Flexible Modeling of Frailty Effects in Clustered Survival Data, Maximum likelihood inference for the Cox regression model with applications to missing covariates, A Semiparametric Bayesian Approach for the Analysis of Competing Risks Data, Statistical inference of Marshall-Olkin bivariate Weibull distribution with three shocks based on progressive interval censored data, Analysis of a semiparametric mixture model for competing risks, Model selection for zero-inflated regression with missing covariates, A Nonparametric Frailty Model for Clustered Survival Data, A Semiparametric Mixture Model for Analyzing Clustered Competing Risks Data, Stochastic EM algorithm of a finite mixture model from hurdle Poisson distribution with missing responses

Cites Work

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• Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
• Ferguson distributions via Polya urn schemes
• Score test of homogeneity for survival data. (With discussion)
• A Bayesian analysis of some nonparametric problems
• Covariate measurement errors in frailty models for clustered survival data
• Omnibus Tests for Comparison of Competing Risks with Adjustment for Covariate Effects
• Frailty Models with Missing Covariates
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Nonparametric Estimation from Incomplete Observations
• Competing risk analyses with special reference to matched pair experiments
• A Covariate Model for Partially Censored Data Subject to Competing Causes of Failure
• Survival analyses in twin studies and matched pair experiments
• Independent and Identically Distributed Monte Carlo Algorithms for Semiparametric Linear Mixed Models
• Prediction of Cumulative Incidence Function under the Proportional Hazards Model
• Estimating Equations with Incomplete Categorical Covariates in the Cox Model
• Estimating the Parameters in the Cox Model When Covariate Variables are Measured with Error
• The Identifiability of the Mixed Proportional Hazards Competing Risks Model
• A Semiparametric Mixture Model for Analyzing Clustered Competing Risks Data