IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v42y2022i5p649-660.html
   My bibliography  Save this article

Prioritizing Research in an Era of Personalized Medicine: The Potential Value of Unexplained Heterogeneity

Author

Listed:
  • Anna Heath

    (Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada
    Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
    Department of Statistical Science, University College London, London, UK)

  • Petros Pechlivanoglou

    (Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada
    Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada)

Abstract

Background Clinical care is moving from a “one size fits all†approach to a setting in which treatment decisions are based on individual treatment response, needs, preferences, and risk. Research into personalized treatment strategies aims to discover currently unknown markers that identify individuals who would benefit from treatments that are nonoptimal at the population level. Before investing in research to identify these markers, it is important to assess whether such research has the potential to generate value. Thus, this article aims to develop a framework to prioritize research into the development of new personalized treatment strategies by creating a set of measures that assess the value of personalizing care based on a set of unknown patient characteristics. Methods Generalizing ideas from the value of heterogeneity framework, we demonstrate 3 measures that assess the value of developing personalized treatment strategies. The first measure identifies the potential value of personalizing medicine within a given disease area. The next 2 measures highlight specific research priorities and subgroup structures that would lead to improved patient outcomes from the personalization of treatment decisions. Results We graphically present the 3 measures to perform sensitivity analyses around the key drivers of value, in particular, the correlation between the individual treatment benefits across the available treatment options. We illustrate these 3 measures using a previously published decision model and discuss how they can direct research in personalized medicine. Conclusion We discuss 3 measures that form the basis of a novel framework to prioritize research into novel personalized treatment strategies. Our novel framework ensures that research targets personalized treatment strategies that have high potential to improve patient outcomes and health system efficiency. Highlights It is important to undertake research prioritization before conducting any research that aims to discover novel methods (e.g., biomarkers) for personalizing treatment. The value of unexplained heterogeneity can highlight disease areas in which personalizing treatment can be valuable and determine key priorities within that area. These priorities can be determined under assumptions of the magnitude of the individual-level treatment effect, which we explore in sensitivity analyses.

Suggested Citation

  • Anna Heath & Petros Pechlivanoglou, 2022. "Prioritizing Research in an Era of Personalized Medicine: The Potential Value of Unexplained Heterogeneity," Medical Decision Making, , vol. 42(5), pages 649-660, July.
    • Handle: RePEc:sae:medema:v:42:y:2022:i:5:p:649-660
    DOI: 10.1177/0272989X211072858
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X211072858
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X211072858?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Eline M. Krijkamp & Fernando Alarid-Escudero & Eva A. Enns & Hawre J. Jalal & M. G. Myriam Hunink & Petros Pechlivanoglou, 2018. "Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial," Medical Decision Making, , vol. 38(3), pages 400-422, April.
    2. Douglas Coyle & Martin J. Buxton & Bernie J. O'Brien, 2003. "Stratified cost‐effectiveness analysis: a framework for establishing efficient limited use criteria," Health Economics, John Wiley & Sons, Ltd., vol. 12(5), pages 421-427, May.
    3. Karl Claxton, 1999. "Bayesian approaches to the value of information: implications for the regulation of new pharmaceuticals," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 269-274, May.
    4. Claxton, Karl, 1999. "The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies," Journal of Health Economics, Elsevier, vol. 18(3), pages 341-364, June.
    5. Anirban Basu & David Meltzer, 2007. "Value of Information on Preference Heterogeneity and Individualized Care," Medical Decision Making, , vol. 27(2), pages 112-127, March.
    6. James C. Felli & Gordon B. Hazen, 1998. "Sensitivity Analysis and the Expected Value of Perfect Information," Medical Decision Making, , vol. 18(1), pages 95-109, January.
    7. A. E. Ades & G. Lu & K. Claxton, 2004. "Expected Value of Sample Information Calculations in Medical Decision Modeling," Medical Decision Making, , vol. 24(2), pages 207-227, March.
    8. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2008. "The Value of Implementation and the Value of Information: Combined and Uneven Development," Medical Decision Making, , vol. 28(1), pages 21-32, January.
    9. Karl Claxton & Mark Sculpher & Chris McCabe & Andrew Briggs & Ron Akehurst & Martin Buxton & John Brazier & Tony O'Hagan, 2005. "Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 339-347, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Laura A. B. Wilson & Susanne R. K. Zajitschek & Malgorzata Lagisz & Jeremy Mason & Hamed Haselimashhadi & Shinichi Nakagawa, 2022. "Sex differences in allometry for phenotypic traits in mice indicate that females are not scaled males," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alan Brennan & Samer A. Kharroubi, 2007. "Expected value of sample information for Weibull survival data," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1205-1225, November.
    2. Alan Brennan & Samer A. Kharroubi, 2007. "Expected value of sample information for Weibull survival data," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1205-1225.
    3. Qi Cao & Erik Buskens & Hans L. Hillege & Tiny Jaarsma & Maarten Postma & Douwe Postmus, 2019. "Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(3), pages 475-482, April.
    4. McKenna, Claire & Chalabi, Zaid & Epstein, David & Claxton, Karl, 2010. "Budgetary policies and available actions: A generalisation of decision rules for allocation and research decisions," Journal of Health Economics, Elsevier, vol. 29(1), pages 170-181, January.
    5. Rachael L. Fleurence, 2007. "Setting priorities for research: a practical application of 'payback' and expected value of information," Health Economics, John Wiley & Sons, Ltd., vol. 16(12), pages 1345-1357.
    6. Stefano Conti & Karl Claxton, 2008. "Dimensions of design space: a decision-theoretic approach to optimal research design," Working Papers 038cherp, Centre for Health Economics, University of York.
    7. Anna Heath, 2022. "Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation," Medical Decision Making, , vol. 42(5), pages 626-636, July.
    8. Karl Claxton & Stephen Palmer & Louise Longworth & Laura Bojke & Susan Griffin & Claire McKenna & Marta Soares & Eldon Spackman & Jihee Youn, 2011. "Uncertainty, evidence and irrecoverable costs: Informing approval, pricing and research decisions for health technologies," Working Papers 069cherp, Centre for Health Economics, University of York.
    9. Brennan, Alan & Kharroubi, Samer A., 2007. "Efficient computation of partial expected value of sample information using Bayesian approximation," Journal of Health Economics, Elsevier, vol. 26(1), pages 122-148, January.
    10. Haitham Tuffaha & Shelley Roberts & Wendy Chaboyer & Louisa Gordon & Paul Scuffham, 2015. "Cost-Effectiveness and Value of Information Analysis of Nutritional Support for Preventing Pressure Ulcers in High-risk Patients: Implement Now, Research Later," Applied Health Economics and Health Policy, Springer, vol. 13(2), pages 167-179, April.
    11. Lauren E. Cipriano & Thomas A. Weber, 2018. "Population-level intervention and information collection in dynamic healthcare policy," Health Care Management Science, Springer, vol. 21(4), pages 604-631, December.
    12. Oakley, Jeremy E. & Brennan, Alan & Tappenden, Paul & Chilcott, Jim, 2010. "Simulation sample sizes for Monte Carlo partial EVPI calculations," Journal of Health Economics, Elsevier, vol. 29(3), pages 468-477, May.
    13. K. Claxton & P. J. Neumannn & S. S. Araki & M. C. Weinstein, "undated". "Bayesian Value-of-Information Analysis: An Application to a Policy Model of Alzheimer's Disease," Discussion Papers 00/39, Department of Economics, University of York.
    14. Rachael L. Fleurence, 2007. "Setting priorities for research: a practical application of ‘payback’ and expected value of information," Health Economics, John Wiley & Sons, Ltd., vol. 16(12), pages 1345-1357, December.
    15. Claxton, K. & Thompson, K. M., 2001. "A dynamic programming approach to the efficient design of clinical trials," Journal of Health Economics, Elsevier, vol. 20(5), pages 797-822, September.
    16. Oren Shavit & Moshe Leshno & Assaf Goldberger & Amir Shmueli & Amnon Hoffman, 2007. "It’s Time to Choose the Study Design!," PharmacoEconomics, Springer, vol. 25(11), pages 903-911, November.
    17. Isaac Corro Ramos & Maureen P. M. H. Rutten-van Mölken & Maiwenn J. Al, 2013. "The Role of Value-of-Information Analysis in a Health Care Research Priority Setting," Medical Decision Making, , vol. 33(4), pages 472-489, May.
    18. Fernando Alarid-Escudero & Eline Krijkamp & Eva A. Enns & Alan Yang & M. G. Myriam Hunink & Petros Pechlivanoglou & Hawre Jalal, 2023. "A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example," Medical Decision Making, , vol. 43(1), pages 21-41, January.
    19. Niklas Zethraeus & Magnus Johannesson & Bengt Jönsson & Mickael Löthgren & Magnus Tambour, 2003. "Advantages of Using the Net-Benefit Approach for Analysing Uncertainty in Economic Evaluation Studies," PharmacoEconomics, Springer, vol. 21(1), pages 39-48, January.
    20. Charles F. Manski, 2022. "Patient‐centered appraisal of race‐free clinical risk assessment," Health Economics, John Wiley & Sons, Ltd., vol. 31(10), pages 2109-2114, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:42:y:2022:i:5:p:649-660. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.