Stratified, Personalised and Precision Medicine

Overview

Contact: Andrea MancaCynthia Iglesias

One of the most topical issues policy makers around the world have to deal with is the decision over whether or not to fund new health technologies when their uptake promise improved patient outcomes at an additional cost for the healthcare system compared to standard care. Several health technology assessment (HTA) agencies have now embraced the use of cost-effectiveness analysis (CEA) as a tool to inform technology adoption decisions in their jurisdictions.

Traditionally, CEA for HTA has focussed on reporting results in terms of averages, either within each treatment group (e.g. mean outcomes) or between treatment groups (e.g. difference in mean outcomes), expressing average treatment effects (ATEs) either on an absolute scale or on a relative scale. This approach implicitly implies a one-size-fits-all notion to (treatment and resource allocation) decision-making, which may overlook important variations in costs and health outcomes occurring between individuals. It is well known that in the presence of between-patient heterogeneity it is possible for the optimal treatment decision to differ between patient strata, and so it is the case for funding decisions. Aside from the important ethical implications of subjecting individuals to unnecessary, ineffective and potentially harmful therapies, ignoring between-patient heterogeneity in favour of one-size-fits-all population-average treatment decisions may imply an inefficient use of healthcare resources. Where the optimal treatment decision differs between patients' strata, a more nuanced decision-making process that takes into account this information has the potential to yield greater population health benefits (for a given budget expenditure) than standard, population-average, decisions.  

Depending upon whether and how heterogeneity is handled in CEA, there are three potential scenarios that may emerge in terms of the decision making they relate to: (a) population average decisions (i.e. one-size-fits-all); (b) stratified decisions (i.e. by patients' sub-group); (c) individualised (personalised or person-centred) decisions, where each individual is a single stratum.

Personalised medicine is becoming one of the most debated topics on the public and private health agenda worldwide. It has supporters among the industry, patient organisations, healthcare professionals, academics, funders and politicians.  Devoting energies and resources to pursue (and hopefully realise) the promises of person-centred healthcare would seem to be a win-win strategy for a number of stakeholders.

Our research in this area include:

  • Exploration of the key factors that drive heterogeneity in value for money considerations
  • Methodological developments to address the challenges faced in the design, conduct and analysis of studies aimed to inform more nuanced decision making policies in healthcare
  • Evaluation of digital health technologies to support person-centred healthcare
  • The development and evaluation of shared decision making and patient decision support tools
  • Elicitation and measurement of stakeholder’s preferences towards person-centred healthcare
  • Development and evaluation of preventive, predictive, prognostic and participatory health technologies for stratified and personalised medicine

Publications

2019

Alexander H, Patton T, Jabbar-Lopez Z, Manca A, Flohr C. Novel systemic therapies in atopic dermatitis: What do we need to fulfil the promise of a treatment revolution? F1000Research 2019;8(F1000 Faculty Rev):132. Download from F1000

Saramago P, Espinoza MA, Sutton AJ, Manca A, Claxton K. The value of further research: The added value of individual-participant level data. Applied Health Economics and Health Policy 2019;17(3):273-284. Download from Springer

2018

Espinoza M, Manca AClaxton KSculpher M. Social value and individual choice: the value of a choice-based decision making process in a collectively funded health system. Health Economics 2018;27(2):E28-40. Download from Wiley

Love-Koh J, Peel A, Rejon-Parilla JC, Ennis K, Lovett R, Manca A, Chalkidou A, Wood H, Taylor M. The future of precision medicine: Potential impacts for Health Technology Assessment. Pharmacoeconomics 2018;36(12):1439-1451.

2017

Patton T, Bojke L, Walton M, Manca A, Helliwell P. Evaluating the cost-effectiveness of biologic treatments for psoriatic arthritis: can we make better use of patient data registries? Clinical Rheumatology 2017;36(8):1803-1810.

2016

Iglesias CP, Erdem S, Birks Y, Taylor SJ, Richardson G, Bower P, van den Berg B and Manca A.  Exploring and quantifying preferences towards self-management support interventions: a mixed-methods survey among individuals with long term health conditions, Report to the Health Foundation, 2016.

2015

IJzerman M, Manca A, Keizer J, Ramsey S.  Implementation of Comparative Effectiveness Research trials in personalized medicine applications in oncology: current perspectives, Journal of Comparative Effectiveness Research, 2015; (5): 65-72 Available from: https://www.dovepress.com/implementation-of-comparative-effectiveness-research-in-personalized-m-peer-reviewed-fulltext-article-CER#

Rogowski W, Payne K, Schnell-Inderst P, Manca A, Rochau U, Jahn B et al. Concepts of ‘personalization’ in personalized medicine: implications for economic evaluation. Pharmacoeconomics. 2015 Jan;33(1):49-59. Available from: 10.1007/s40273-014-0211-5

2014

Gavan S, Harrison M, Iglesias Urrutia CP, Barton A, Manca A, Payne K. Economics of stratified medicine in rheumatoid arthritis. Current Rheumatology Reports. 2014 Nov 4;16:468. Available from: 10.1007/s11926-014-0468-x

Espinoza MA, Manca A, Claxton K, Sculpher MJ. The Value of Heterogeneity for Cost-Effectiveness Subgroup Analysis: Conceptual Framework and Application. Medical Decision Making. 2014 Nov;34(8):951-964. Available from: 10.1177/0272989X14538705

Espinoza MA, Sculpher M, Basu A, Manca A. Analysing heterogeneity to support decision making. In Culyer T (editor), Encyclopedia of Health Economics. Elsevier. 2014. p. 71-76. Available from: 10.1016/B978-0-12-375678-7.01420-6

Projects

Goettsch W (PI), Manca A, Iglesias Urrutia CP, Kreif N, Rothery C, Smith AG, Yu G. HTx:Next Generation HTA. EC Horizon 2020 (2019 - 2023)

Manca A, Patton TE. Atopic Eczema (AE) in adults & children. British Skin Foundation (2017 - 2020) 

deWitte T (PI), Manca A, et al. Providing the right care to the right patient with MyeloDysplastic Syndrome at the right time (MDS-RIGHT). EC Horizon 2020 (2015-2019)

Peek N (PI), Manca A, et al. The Wearable Clinic: Connecting Health, Self and Care. Electronics and Physics Research Council (EPSRC), (2017-2020)

Manca A (PI). Research agreement between University of York (i.e. DoHS and CHE) and the Luxemburg Institute of Health (LIH), to foster cooperation on the Economics of Personalised Medicine (2015-2019).

Hernandez J (PI), Manca A et al. Healthcare Alliance for Resourceful Medicines Offensive against Neoplasms in HematologY (HARMONY). H2020 IMI2 (2016-2021)

Roman E (PI), Manca A, et al. Facilitating informed decision-making in haemato-oncology. NIHR PGfAR, (2016-2019)

Manca A (PI), et al. A study into the operation of discrete choices to understand how people value self management support interventions. Health Foundation (2014-2015)

Sandhu H (PI), Manca A, et al. Improving the Wellbeing of people with Opioid Treated CHronic pain (I-WOTCH), NIHR HTA programme (2016-2020)

Hansson MG (PI), Manca A (Co-I), et al. Mind the risk – Ethical, psychological and social implications of provision of risk information from genetic and related technologies – A joint European research program. The Swedish Foundation for the Humanities in Social Science - (2014-2019)