Analysis of multivariate survival data with Clayton regression models under conditional and marginal formulations

DOI10.1016/J.CSDA.2014.01.001zbMath1506.62079OpenAlexW1974554567MaRDI QIDQ1623446

Y. Aharonov

Publication date: 23 November 2018

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

Full work available at URL: https://doi.org/10.1016/j.csda.2014.01.001

zbMATH Keywords

conditional modelsmarginal modelsmultivariate survival dataClayton modelscovariate coefficient

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Estimation in survival analysis and censored data (62N02)

Cites Work

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• Modeling survival data: extending the Cox model
• Analysis of multivariate survival data
• A corrected likelihood method for the proportional hazards model with covariates subject to measurement error
• The Aalen additive gamma frailty hazards model
• Approximation Theorems of Mathematical Statistics
• Flexible Maximum Likelihood Methods for Bivariate Proportional Hazards Models
• Gamma frailty transformation models for multivariate survival times
• Multivariate Generalizations of the Proportional Hazards Model
• Bivariate Survival Models Induced by Frailties
• A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence
• Analysis of interval-grouped recurrent-event data using piecewise constant rate functions
• Measurement Error in Nonlinear Models

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