Hierarchical Proportional Hazards Regression Models for Highly Stratified Data

DOI10.1111/j.0006-341X.1999.01162.xzbMath1059.62624WikidataQ30649823 ScholiaQ30649823MaRDI QIDQ4668326

James S. Hodges, Bradley P. Carlin

Publication date: 18 April 2005

Published in: Biometrics (Search for Journal in Brave)

zbMATH Keywords

partial likelihoodMarkov chain Monte CarloCox modelBayesian methodsbaseline hazard function

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Point estimation (62F10) Estimation in survival analysis and censored data (62N02)

Related Items (12)

Semiparametric Bayesian analysis of high-dimensional censored outcome data ⋮ Integrated likelihoods in parametric survival models for highly clustered censored data ⋮ Bayesian semiparametric modeling of survival data based on mixtures of \(B\)-spline distributions ⋮ Monte Carlo modified profile likelihood in models for clustered data ⋮ Bayesian Spatial Survival Models ⋮ Fully Nonparametric Regression Modelling of Misclassified Censored Time-to-Event Data ⋮ Adaptive lasso for accelerated hazards models ⋮ Flexible link continual reassessment methods for trivariate binary outcome phase I/II trials ⋮ Semiparametric regression control charts ⋮ Semiparametric proportional odds models for spatially correlated survival data ⋮ Multivariate parametric spatiotemporal models for county level breast cancer survival data ⋮ Hierarchical failure time regression using mixtures for classification of the immune response of atlantic salmon

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