Abstract
Introduction: We develop a framework to model disease progression across Alzheimer's disease (AD) and to assess the cost-effectiveness of future disease-modifying therapies (DMTs) for people with mild cognitive impairment (MCI) due to AD.
Methods: Using data from the US National Alzheimer's Coordinating Center, we apply survival analysis to estimate transition from predementia to AD dementia and ordered probit regression to estimate transitions across AD dementia stages. We investigate the cost-effectiveness of a hypothetical treatment scenario for people in MCI due to AD.
Results: We present an open-access model-based decision-analytic framework. Assuming a modest DMT treatment effect in MCI, we predict extended life expectancy and a reduction in time with AD dementia.
Discussion: Any future DMT for AD is expected to pose significant economic challenges across all health-care systems, and decision-analytic modeling will be required to assess costs and outcomes. Further developments are needed to inform these health policy considerations. (C) 2019 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association.
| Original language | English |
|---|---|
| Pages (from-to) | 1309-1321 |
| Number of pages | 13 |
| Journal | Alzheimer's & Dementia |
| Volume | 15 |
| Issue number | 10 |
| DOIs | |
| Publication status | Published - Oct 2019 |
Keywords
- Alzheimer's disease
- Dementia
- Mild cognitive impairment
- Decision-analytic modeling
- Progression
- Prediction
- Health policy
- Cost-effectiveness analysis
- MILD COGNITIVE IMPAIRMENT
- ASSOCIATION WORKGROUPS
- DIAGNOSTIC GUIDELINES
- ECONOMIC-EVALUATION
- NATIONAL INSTITUTE
- DEMENTIA
- PROGRESSION
- MORTALITY
- CARE
- RECOMMENDATIONS
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In: Alzheimer's & Dementia, Vol. 15, No. 10, 10.2019, p. 1309-1321.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Assessing cost-effectiveness of early intervention in Alzheimer's disease
T2 - An open-source modeling framework
AU - Green, Colin
AU - Handels, Ron
AU - Gustavsson, Anders
AU - Wimo, Anders
AU - Winblad, Bengt
AU - Skoldunger, Anders
AU - Jonsson, Linus
N1 - Funding Information: We would like to thank The National Alzheimer's Disease Coordinating Center (NACC) for access to NACC data. The NACC database is funded by National Institute on Aging / National Institutes of Health Grant U01 AG016976 . NACC data are contributed by the NIA funded ADCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Steven Ferris, PhD), P30 AG013854 (PI M. Marsel Mesulam, MD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG016570 (PI Marie-Francoise Chesselet, MD, PhD), P50 AG005131 (PI Douglas Galasko, MD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG028383 (PI Linda Van Eldik, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P50 AG005136 (PI Thomas Montine, MD, PhD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), and P50 AG047270 (PI Stephen Strittmatter, MD, PhD). Funding Information: Conflict of Interest: B.W. acknowledges financial support from the Swedish Research Council, No 2018-02843 and from Margaretha af Ugglas' Foundation. L.J. is an employee of H. Lundbeck, a pharmaceutical company that develops and markets therapies for Alzheimer's disease. R.H. acknowledges financial support received from the Dutch Alzheimer's Association, ?Alzheimer Nederland?, against grant number WE.15-2016-09. A.G. is a partner of Quantify Research, providing consultancy services to pharmaceutical companies and other private and public organizations and institutions.We would like to thank The National Alzheimer's Disease Coordinating Center (NACC) for access to NACC data. The NACC database is funded by National Institute on Aging/National Institutes of Health Grant U01 AG016976. NACC data are contributed by the NIA funded ADCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Steven Ferris, PhD), P30 AG013854 (PI M. Marsel Mesulam, MD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG016570 (PI Marie-Francoise Chesselet, MD, PhD), P50 AG005131 (PI Douglas Galasko, MD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG028383 (PI Linda Van Eldik, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P50 AG005136 (PI Thomas Montine, MD, PhD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), and P50 AG047270 (PI Stephen Strittmatter, MD, PhD). An earlier summary version of the research (method) approach and model structure used in this paper was presented and discussed at the 8th International Pharmacoeconomic Conference on Alzheimer's Disease (IPECAD) meeting, Paris, France, February 15th-16th 2018. We would like to thank Leala Watson, University of Exeter, UK, for assistance in formatting the manuscript. We would like to thank Gunilla Johansson, Karolinska Institute, Stockholm, Sweden, for administrative support related to the development of research associated with the manuscript. Publisher Copyright: © 2019 The Authors
PY - 2019/10
Y1 - 2019/10
N2 - Introduction: We develop a framework to model disease progression across Alzheimer's disease (AD) and to assess the cost-effectiveness of future disease-modifying therapies (DMTs) for people with mild cognitive impairment (MCI) due to AD.Methods: Using data from the US National Alzheimer's Coordinating Center, we apply survival analysis to estimate transition from predementia to AD dementia and ordered probit regression to estimate transitions across AD dementia stages. We investigate the cost-effectiveness of a hypothetical treatment scenario for people in MCI due to AD.Results: We present an open-access model-based decision-analytic framework. Assuming a modest DMT treatment effect in MCI, we predict extended life expectancy and a reduction in time with AD dementia.Discussion: Any future DMT for AD is expected to pose significant economic challenges across all health-care systems, and decision-analytic modeling will be required to assess costs and outcomes. Further developments are needed to inform these health policy considerations. (C) 2019 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association.
AB - Introduction: We develop a framework to model disease progression across Alzheimer's disease (AD) and to assess the cost-effectiveness of future disease-modifying therapies (DMTs) for people with mild cognitive impairment (MCI) due to AD.Methods: Using data from the US National Alzheimer's Coordinating Center, we apply survival analysis to estimate transition from predementia to AD dementia and ordered probit regression to estimate transitions across AD dementia stages. We investigate the cost-effectiveness of a hypothetical treatment scenario for people in MCI due to AD.Results: We present an open-access model-based decision-analytic framework. Assuming a modest DMT treatment effect in MCI, we predict extended life expectancy and a reduction in time with AD dementia.Discussion: Any future DMT for AD is expected to pose significant economic challenges across all health-care systems, and decision-analytic modeling will be required to assess costs and outcomes. Further developments are needed to inform these health policy considerations. (C) 2019 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association.
KW - Alzheimer's disease
KW - Dementia
KW - Mild cognitive impairment
KW - Decision-analytic modeling
KW - Progression
KW - Prediction
KW - Health policy
KW - Cost-effectiveness analysis
KW - MILD COGNITIVE IMPAIRMENT
KW - ASSOCIATION WORKGROUPS
KW - DIAGNOSTIC GUIDELINES
KW - ECONOMIC-EVALUATION
KW - NATIONAL INSTITUTE
KW - DEMENTIA
KW - PROGRESSION
KW - MORTALITY
KW - CARE
KW - RECOMMENDATIONS
U2 - 10.1016/j.jalz.2019.05.004
DO - 10.1016/j.jalz.2019.05.004
M3 - Article
C2 - 31402324
SN - 1552-5260
VL - 15
SP - 1309
EP - 1321
JO - Alzheimer's & Dementia
JF - Alzheimer's & Dementia
IS - 10
ER -