State selection in Markov models for panel data with application to psoriatic arthritis. (5th March 2015)
- Record Type:
- Journal Article
- Title:
- State selection in Markov models for panel data with application to psoriatic arthritis. (5th March 2015)
- Main Title:
- State selection in Markov models for panel data with application to psoriatic arthritis
- Authors:
- Thom, Howard H. Z.
Jackson, Christopher H.
Commenges, Daniel
Sharples, Linda D. - Abstract:
- <abstract abstract-type="main" id="sim6460-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="sim6460-para-0001">Markov multistate models in continuous‐time are commonly used to understand the progression over time of disease or the effect of treatments and covariates on patient outcomes. The states in multistate models are related to categorisations of the disease status, but there is often uncertainty about the number of categories to use and how to define them. Many categorisations, and therefore multistate models with different states, may be possible. Different multistate models can show differences in the effects of covariates or in the time to events, such as death, hospitalisation, or disease progression. Furthermore, different categorisations contain different quantities of information, so that the corresponding likelihoods are on different scales, and standard, likelihood‐based model comparison is not applicable.</p> <p id="sim6460-para-0002">We adapt a recently developed modification of Akaike's criterion, and a cross‐validatory criterion, to compare the predictive ability of multistate models on the information which they share. All the models we consider are fitted to data consisting of observations of the process at arbitrary times, often called 'panel' data. We develop an implementation of these criteria through Hidden Markov models and apply them to the comparison of multistate models for the Health Assessment Questionnaire score in<abstract abstract-type="main" id="sim6460-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="sim6460-para-0001">Markov multistate models in continuous‐time are commonly used to understand the progression over time of disease or the effect of treatments and covariates on patient outcomes. The states in multistate models are related to categorisations of the disease status, but there is often uncertainty about the number of categories to use and how to define them. Many categorisations, and therefore multistate models with different states, may be possible. Different multistate models can show differences in the effects of covariates or in the time to events, such as death, hospitalisation, or disease progression. Furthermore, different categorisations contain different quantities of information, so that the corresponding likelihoods are on different scales, and standard, likelihood‐based model comparison is not applicable.</p> <p id="sim6460-para-0002">We adapt a recently developed modification of Akaike's criterion, and a cross‐validatory criterion, to compare the predictive ability of multistate models on the information which they share. All the models we consider are fitted to data consisting of observations of the process at arbitrary times, often called 'panel' data. We develop an implementation of these criteria through Hidden Markov models and apply them to the comparison of multistate models for the Health Assessment Questionnaire score in psoriatic arthritis. This procedure is straightforward to implement in the R package 'msm'. Copyright © 2015 John Wiley &amp; Sons, Ltd.</p> </abstract> … (more)
- Is Part Of:
- Statistics in medicine. Volume 34:Number 16(2015)
- Journal:
- Statistics in medicine
- Issue:
- Volume 34:Number 16(2015)
- Issue Display:
- Volume 34, Issue 16 (2015)
- Year:
- 2015
- Volume:
- 34
- Issue:
- 16
- Issue Sort Value:
- 2015-0034-0016-0000
- Page Start:
- 2456
- Page End:
- 2475
- Publication Date:
- 2015-03-05
- Subjects:
- Medical statistics -- Periodicals
Statistique médicale -- Périodiques
Statistiques médicales -- Périodiques
610.727 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/sim.6460 ↗
- Languages:
- English
- ISSNs:
- 0277-6715
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8453.576000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 3573.xml