Cargando…

Using dynamic microsimulation to project cognitive function in the elderly population

BACKGROUND: A long-term projection model based on nationally representative data and tracking disease progression across Alzheimer’s disease continuum is important for economics evaluation of Alzheimer’s disease and other dementias (ADOD) therapy. METHODS: The Health and Retirement Study (HRS) inclu...

Descripción completa

Detalles Bibliográficos
Autores principales: Wei, Yifan, Heun-Johnson, Hanke, Tysinger, Bryan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477290/
https://www.ncbi.nlm.nih.gov/pubmed/36107946
http://dx.doi.org/10.1371/journal.pone.0274417
_version_ 1784790326203908096
author Wei, Yifan
Heun-Johnson, Hanke
Tysinger, Bryan
author_facet Wei, Yifan
Heun-Johnson, Hanke
Tysinger, Bryan
author_sort Wei, Yifan
collection PubMed
description BACKGROUND: A long-term projection model based on nationally representative data and tracking disease progression across Alzheimer’s disease continuum is important for economics evaluation of Alzheimer’s disease and other dementias (ADOD) therapy. METHODS: The Health and Retirement Study (HRS) includes an adapted version of the Telephone Interview for Cognitive Status (TICS27) to evaluate respondents’ cognitive function. We developed an ordered probit transition model to predict future TICS27 score. This transition model is utilized in the Future Elderly Model (FEM), a dynamic microsimulation model of health and health-related economic outcomes for the US population. We validated the FEM TICS27 model using a five-fold cross validation approach, by comparing 10-year (2006–2016) simulated outcomes against observed HRS data. RESULTS: In aggregate, the distribution of TICS27 scores after ten years of FEM simulation matches the HRS. FEM’s assignment of cognitive/mortality status also matches those observed in HRS on the population level. At the individual level, the area under the receiver operating characteristic (AUROC) curve is 0.904 for prediction of dementia or dead with dementia in 10 years, the AUROC for predicting significant cognitive decline in two years for mild cognitive impairment patients is 0.722. CONCLUSIONS: The FEM TICS27 model demonstrates its predictive accuracy for both two- and ten-year cognitive outcomes. Our cognition projection model is unique in its validation with an unbiased approach, resulting in a high-quality platform for assessing the burden of cognitive decline and translating the benefit of innovative therapies into long-term value to society.
format Online
Article
Text
id pubmed-9477290
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-94772902022-09-16 Using dynamic microsimulation to project cognitive function in the elderly population Wei, Yifan Heun-Johnson, Hanke Tysinger, Bryan PLoS One Research Article BACKGROUND: A long-term projection model based on nationally representative data and tracking disease progression across Alzheimer’s disease continuum is important for economics evaluation of Alzheimer’s disease and other dementias (ADOD) therapy. METHODS: The Health and Retirement Study (HRS) includes an adapted version of the Telephone Interview for Cognitive Status (TICS27) to evaluate respondents’ cognitive function. We developed an ordered probit transition model to predict future TICS27 score. This transition model is utilized in the Future Elderly Model (FEM), a dynamic microsimulation model of health and health-related economic outcomes for the US population. We validated the FEM TICS27 model using a five-fold cross validation approach, by comparing 10-year (2006–2016) simulated outcomes against observed HRS data. RESULTS: In aggregate, the distribution of TICS27 scores after ten years of FEM simulation matches the HRS. FEM’s assignment of cognitive/mortality status also matches those observed in HRS on the population level. At the individual level, the area under the receiver operating characteristic (AUROC) curve is 0.904 for prediction of dementia or dead with dementia in 10 years, the AUROC for predicting significant cognitive decline in two years for mild cognitive impairment patients is 0.722. CONCLUSIONS: The FEM TICS27 model demonstrates its predictive accuracy for both two- and ten-year cognitive outcomes. Our cognition projection model is unique in its validation with an unbiased approach, resulting in a high-quality platform for assessing the burden of cognitive decline and translating the benefit of innovative therapies into long-term value to society. Public Library of Science 2022-09-15 /pmc/articles/PMC9477290/ /pubmed/36107946 http://dx.doi.org/10.1371/journal.pone.0274417 Text en © 2022 Wei et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wei, Yifan
Heun-Johnson, Hanke
Tysinger, Bryan
Using dynamic microsimulation to project cognitive function in the elderly population
title Using dynamic microsimulation to project cognitive function in the elderly population
title_full Using dynamic microsimulation to project cognitive function in the elderly population
title_fullStr Using dynamic microsimulation to project cognitive function in the elderly population
title_full_unstemmed Using dynamic microsimulation to project cognitive function in the elderly population
title_short Using dynamic microsimulation to project cognitive function in the elderly population
title_sort using dynamic microsimulation to project cognitive function in the elderly population
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477290/
https://www.ncbi.nlm.nih.gov/pubmed/36107946
http://dx.doi.org/10.1371/journal.pone.0274417
work_keys_str_mv AT weiyifan usingdynamicmicrosimulationtoprojectcognitivefunctionintheelderlypopulation
AT heunjohnsonhanke usingdynamicmicrosimulationtoprojectcognitivefunctionintheelderlypopulation
AT tysingerbryan usingdynamicmicrosimulationtoprojectcognitivefunctionintheelderlypopulation