Multi-State Models for Disease Natural History

A variety of disease processes can be described through transitions between discrete states, such as stable or accelerated disease state in leukemia; development of AIDS-defining illnesses in HIV; or diminished lung function in asthma patients. In progressive diseases, subjects traverse disease state...

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Bibliographic Details
Published inCurrent Trends in Bayesian Methodology with Applications pp. 407 - 430
Format Book Chapter
LanguageEnglish
Published United Kingdom Chapman and Hall/CRC 2015
CRC Press LLC
Subjects
BIOLOGY, LIFE SCIENCES
Probability & statistics
Panel Observation
Illness Death Model
semi-Markov Models
Transition Intensities
Sojourn Times
Homogeneous semi-Markov Process
Time Transformation Function
Markov Assumption
Multi-state Models
Markov Renewal Processes
Birth Death Process
Conditional Sojourn Time
Homogeneous Markov Chain
Homogeneous Markov Process
semi-Markov Process
Nonhomogeneous Markov
MAR Assumption
Transition Probability Matrix
Progressive Disease
Markov Chain
Markov Process
Weibull Models
Embedded Markov Chain
Sojourn Time Distribution
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ISBN1482235110
9781482235111
DOI10.1201/b18502-25

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Summary:A variety of disease processes can be described through transitions between discrete states, such as stable or accelerated disease state in leukemia; development of AIDS-defining illnesses in HIV; or diminished lung function in asthma patients. In progressive diseases, subjects traverse disease states inin Bayesianwhile in non-progressive diseases it is possible for subjects to experience repeated occurrences of some or all states.
ISBN:1482235110
9781482235111
DOI:10.1201/b18502-25