Combining Cross‐Sectional and Prospective Data Methods to Improve Transition Parameter Estimation for Characterizing the Accumulation of HIV‐1 Drug Resistance Mutations

DOI10.1111/j.1541-0420.2007.00774.xzbMath1146.62088OpenAlexW1966699283WikidataQ31107886 ScholiaQ31107886MaRDI QIDQ5434901

Marcello Pagano, Brian C. Healy, Victor de Gruttola

Publication date: 14 January 2008

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2007.00774.x

zbMATH Keywords

constrained optimizationMarkov modelsbranching treesgenetic pathwaysHIV resistance mutations

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20)

Uses Software

• Mtreemix

Cites Work

• Estimation of models of autoregressive signal plus white noise
• A mutagenetic tree hidden Markov model for longitudinal clonal HIV sequence data
• Hidden Markov models for settings with interval-censored transition times and uncertain time origin: application to HIV genetic analyses
• Estimating the Infinitesimal Generator of a Continuous Time, Finite State Markov Process