Modeling Markers of Disease Progression by a Hidden Markov Process: Application to Characterizing CD4 Cell Decline

DOI10.1111/j.0006-341X.2000.00733.xzbMath1060.62619OpenAlexW1976615842WikidataQ43986083 ScholiaQ43986083MaRDI QIDQ4670405

Sylvia Richardson, Chantal Guihenneuc-Jouyaux, Ira M. jun. Longini

Publication date: 22 April 2005

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.0006-341x.2000.00733.x

zbMATH Keywords

Markov chain Monte CarloMarkov processAIDSmeasurement errorGibbs samplingmultistate modelsBayesian hierarchical modelCD4 cells

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05) Numerical analysis or methods applied to Markov chains (65C40)

Related Items

An MCMC computational approach for a continuous time state-dependent regime switching diffusion process ⋮ Phase-type models for competing risks, with emphasis on identifiability issues ⋮ Continuous time hidden Markov model for longitudinal data ⋮ Dynamic logistic state space prediction model for clinical decision making ⋮ Modeling Nonhomogeneous Markov Processes via Time Transformation ⋮ A multi-stage compartmental model for HIV-infected individuals. I: Waiting time approach ⋮ Bayesian state-space modeling in gene expression data analysis: an application with biomarker prediction ⋮ Mortality modeling and regression with matrix distributions ⋮ A hidden Markov model for informative dropout in longitudinal response data with crisis states ⋮ Random effects models for HIV marker data: practical approaches with currently available software ⋮ Unnamed Item ⋮ Statistical Inference for Partially Hidden Markov Models ⋮ MCMC implementation for Bayesian hidden semi-Markov models with illustrative applications ⋮ Semiparametric time to event models in the presence of error-prone, self-reported outcomes -- with application to the women's health initiative ⋮ Effectiveness of Potent Antiretroviral Therapy on Progression of Human Immunodeficiency Virus: Bayesian Modelling and Model Checking via Counterfactual Replicates ⋮ Markov Aging Process and Phase-Type Law of Mortality

Uses Software

• CODA

Cites Work

• Unnamed Item
• Modeling Disease Market Processes in AIDS
• Sampling-Based Approaches to Calculating Marginal Densities
• A Markov Model for Analysing Cancer Markers and Disease States in Survival Studies
• A Stochastic Model for Analysis of Longitudinal AIDS Data
• Markov Chains With Measurement Error: Estimating the `True' Course of a Marker of the Progression of Human Immunodeficiency Virus Disease
• Age-Specific Incidence and Prevalence: A Statistical Perspective
• Statistical models based on counting processes