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Estimating dementia-free life expectancy for Parkinson's patients using Bayesian inference and microsimulation.
Biostatistics 2009; 10(4):729-43B

Abstract

Interval-censored longitudinal data taken from a Norwegian study of individuals with Parkinson's disease are investigated with respect to the onset of dementia. Of interest are risk factors for dementia and the subdivision of total life expectancy (LE) into LE with and without dementia. To estimate LEs using extrapolation, a parametric continuous-time 3-state illness-death Markov model is presented in a Bayesian framework. The framework is well suited to allow for heterogeneity via random effects and to investigate additional computation using model parameters. In the estimation of LEs, microsimulation is used to take into account random effects. Intensities of moving between the states are allowed to change in a piecewise-constant fashion by linking them to age as a time-dependent covariate. Possible right censoring at the end of the follow-up can be incorporated. The model is applicable in many situations where individuals are followed over a long time period. In describing how a disease develops over time, the model can help to predict future need for health care.

Authors+Show Affiliations

Medical Research Council Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 OSR, UK. ardo.vandenhout@mrc-bsu.cam.ac.ukNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't
Validation Study

Language

eng

PubMed ID

19648228

Citation

van den Hout, Ardo, and Fiona E. Matthews. "Estimating Dementia-free Life Expectancy for Parkinson's Patients Using Bayesian Inference and Microsimulation." Biostatistics (Oxford, England), vol. 10, no. 4, 2009, pp. 729-43.
van den Hout A, Matthews FE. Estimating dementia-free life expectancy for Parkinson's patients using Bayesian inference and microsimulation. Biostatistics. 2009;10(4):729-43.
van den Hout, A., & Matthews, F. E. (2009). Estimating dementia-free life expectancy for Parkinson's patients using Bayesian inference and microsimulation. Biostatistics (Oxford, England), 10(4), pp. 729-43. doi:10.1093/biostatistics/kxp027.
van den Hout A, Matthews FE. Estimating Dementia-free Life Expectancy for Parkinson's Patients Using Bayesian Inference and Microsimulation. Biostatistics. 2009;10(4):729-43. PubMed PMID: 19648228.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Estimating dementia-free life expectancy for Parkinson's patients using Bayesian inference and microsimulation. AU - van den Hout,Ardo, AU - Matthews,Fiona E, Y1 - 2009/07/31/ PY - 2009/8/4/entrez PY - 2009/8/4/pubmed PY - 2009/12/16/medline SP - 729 EP - 43 JF - Biostatistics (Oxford, England) JO - Biostatistics VL - 10 IS - 4 N2 - Interval-censored longitudinal data taken from a Norwegian study of individuals with Parkinson's disease are investigated with respect to the onset of dementia. Of interest are risk factors for dementia and the subdivision of total life expectancy (LE) into LE with and without dementia. To estimate LEs using extrapolation, a parametric continuous-time 3-state illness-death Markov model is presented in a Bayesian framework. The framework is well suited to allow for heterogeneity via random effects and to investigate additional computation using model parameters. In the estimation of LEs, microsimulation is used to take into account random effects. Intensities of moving between the states are allowed to change in a piecewise-constant fashion by linking them to age as a time-dependent covariate. Possible right censoring at the end of the follow-up can be incorporated. The model is applicable in many situations where individuals are followed over a long time period. In describing how a disease develops over time, the model can help to predict future need for health care. SN - 1468-4357 UR - https://www.unboundmedicine.com/medline/citation/19648228/Estimating_dementia_free_life_expectancy_for_Parkinson's_patients_using_Bayesian_inference_and_microsimulation_ L2 - https://academic.oup.com/biostatistics/article-lookup/doi/10.1093/biostatistics/kxp027 DB - PRIME DP - Unbound Medicine ER -