Bayesian Approaches to Joint Cure-Rate and Longitudinal Models with Applications to Cancer Vaccine Trials

DOI10.1111/1541-0420.00079zbMath1210.62145OpenAlexW2079244816WikidataQ47401849 ScholiaQ47401849MaRDI QIDQ3079171

Elizabeth R. Brown, Joseph G. Ibrahim

Publication date: 1 March 2011

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1541-0420.00079

zbMATH Keywords

Markov chain Monte Carlocure modelcancer vaccinesjoint longitudinal and survival modellongitudinal mixture model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Medical applications (general) (92C50) Survival analysis and censored data (62N99)

Related Items

Semiparametric Bayesian joint models of multivariate longitudinal and survival data, A new joint model for longitudinal and survival data with a cure fraction, Bayesian inference on longitudinal-survival data with multiple features, Missing data methods in longitudinal studies: a review, Semiparametric Bayesian Markov analysis of personalized benefit-risk assessment, A semiparametric promotion time cure model with support vector machine, Joint modeling of longitudinal and time-to-event data on multivariate protein biomarkers, Bayesian regression analysis of data with random effects covariates from nonlinear longitudinal measurements, A joint modelling approach for clustered recurrent events and death events, Jointly modeling skew longitudinal survival data with missingness and mismeasured covariates, Bayesian Influence Measures for Joint Models for Longitudinal and Survival Data, A Nested Dirichlet Process Analysis of Cluster Randomized Trial Data With Application in Geriatric Care Assessment, A Bayesian Semiparametric Survival Model with Longitudinal Markers, Bayesian local influence for survival models, Assessing importance of biomarkers: A Bayesian joint modelling approach of longitudinal and survival data with semi-competing risks, Joint modeling of longitudinal and cure-survival data, A new approach for joint modelling of longitudinal measurements and survival times with a cure fraction, A likelihood based approach for joint modeling of longitudinal trajectories and informative censoring process

Cites Work

• Monte Carlo methods in Bayesian computation
• Maximum Likelihood Methods for Cure Rate Models with Missing Covariates
• The Evaluation of Multiple Surrogate Endpoints
• Sampling-Based Approaches to Calculating Marginal Densities
• A competing risks derivation of a mixture model for the analysis of survival data
• A SEMIPARAMETRIC MIXTURE MODEL FOR THE ANALYSIS OF COMPETING RISKS DATA
• A Joint Model for Survival and Longitudinal Data Measured with Error
• A New Bayesian Model for Survival Data with a Surviving Fraction
• Estimation in a Cox Proportional Hazards Cure Model
• Modeling the Relationship of Survival to Longitudinal Data Measured with Error. Applications to Survival and CD4 Counts in Patients with AIDS
• The joint modeling of a longitudinal disease progression marker and the failure time process in the presence of cure
• Bayesian survival analysis