Current issues and future research priorities for health economic modelling across the full continuum of Alzheimer's disease

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Available data and models for the health-economic evaluation of treatment in Alzheimer's disease (AD) have limitations causing uncertainty to decision makers. Forthcoming treatment strategies in preclinical or early AD warrant an update on the challenges associated with their economic evaluation. The perspectives of the co-authors were complemented with a targeted review of literature discussing methodological issues and data gaps in AD health-economic modelling. The methods and data available to translate treatment efficacy in early disease into long-term outcomes of relevance to policy makers and payers are limited. Current long-term large-scale data accurately representing the continuous, multifaceted, and heterogeneous disease process are missing. The potential effect of disease-modifying treatment on key long-term outcomes such as institutionalization and death is uncertain but may have great effect on cost-effectiveness. Future research should give priority to collaborative efforts to access better data on the natural progression of AD and its association with key long-term outcomes.This research was funded by Novartis Pharma AG

Challenges for Optimizing Real-World Evidence in Alzheimer’s Disease: The ROADMAP Project

ROADMAP is a public-private advisory partnership to evaluate the usability of multiple data sources, including real-world evidence, in the decision-making process for new treatments in Alzheimer’s disease, and to advance key concepts in disease and pharmacoeconomic modeling. ROADMAP identified key disease and patient outcomes for stakeholders to make informed funding and treatment decisions, provided advice on data integration methods and standards, and developed conceptual cost-effectiveness and disease models designed in part to assess whether early treatment provides long-term benefit

Ethical and Social Implications of Using Predictive Modeling for Alzheimer's Disease Prevention: A Systematic Literature Review.

BACKGROUND: The therapeutic paradigm in Alzheimer's disease (AD) is shifting from symptoms management toward prevention goals. Secondary prevention requires the identification of individuals without clinical symptoms, yet "at-risk" of developing AD dementia in the future, and thus, the use of predictive modeling. OBJECTIVE: The objective of this study was to review the ethical concerns and social implications generated by this new approach. METHODS: We conducted a systematic literature review in Medline, Embase, PsycInfo, and Scopus, and complemented it with a gray literature search between March and July 2018. Then we analyzed data qualitatively using a thematic analysis technique. RESULTS: We identified thirty-one ethical issues and social concerns corresponding to eight ethical principles: (i) respect for autonomy, (ii) beneficence, (iii) non-maleficence, (iv) equality, justice, and diversity, (v) identity and stigma, (vi) privacy, (vii) accountability, transparency, and professionalism, and (viii) uncertainty avoidance. Much of the literature sees the discovery of disease-modifying treatment as a necessary and sufficient condition to justify AD risk assessment, overlooking future challenges in providing equitable access to it, establishing long-term treatment outcomes and social consequences of this approach, e.g., medicalization. The ethical/social issues associated specifically with predictive models, such as the adequate predictive power and reliability, infrastructural requirements, data privacy, potential for personalized medicine in AD, and limiting access to future AD treatment based on risk stratification, were covered scarcely. CONCLUSION: The ethical discussion needs to advance to reflect recent scientific developments and guide clinical practice now and in the future, so that necessary safeguards are implemented for large-scale AD secondary prevention.</p

Challenges for optimizing real-world evidence in Alzheimer’s disease: The ROADMAP Project

ROADMAP is a public-private advisory partnership to evaluate the usability of multiple data sources, including real-world evidence, in the decision-making process for new treatments in Alzheimer's disease, and to advance key concepts in disease and pharmacoeconomic modeling. ROADMAP identified key disease and patient outcomes for stakeholders to make informed funding and treatment decisions, provided advice on data integration methods and standards, and developed conceptual cost-effectiveness and disease models designed in part to assess whether early treatment provides long-term benefit