IDEAS home Printed from https://ideas.repec.org/a/bpj/fhecpo/v16y2013i2p107-120n1.html
   My bibliography  Save this article

Personalized Medicine in the Context of Comparative Effectiveness Research

Author

Listed:
  • Basu Anirban

    (1959 NE Pacific St., Box 357660, Seattle 98195)

Abstract

The world of patient-centered outcomes research (PCOR) seems to bridge the previously disjointed worlds of comparative effectiveness research (CER) and personalized medicine (PM). Indeed, theoretical reasoning on how information on medical quality should inform decision making, both at the individual and the policy level, reveals that personalized information on the value of medical products is critical for improving decision making at all levels. However, challenges to generating, evaluating and translating evidence that might lead to personalization need to be critically assessed. In this paper, I discuss two different concepts of personalized medicine – passive personalization (PPM) and active personalization (APM) that are important to distinguish in order to invest efficiently in PCOR and develop objective evidence on the value of personalization that will aid in its translation. APM constitutes the process of actively seeking identifiers, which can be genotypical, phenotypical or even environmental, that can be used to differentiate between the marginal benefits of treatment across patients. In contrast, PPM involves a passive approach to personalization where, in the absence of explicit research to discover identifiers, patients and physicians “learn by doing” mostly due to the repeated use of similar products on similar patients. Benchmarking the current state of PPM sets the bar to which the expected value of any new APM agenda should be evaluated. Exploring processes that enable PPM in practice can help discover new APM agendas, such as those based on developing predictive algorithms based on clinical, phenotypical and preference data, which may be more efficient that trying to develop expensive genetic tests. It can also identify scenarios or subgroups of patients where genomic research would be most valuable since alternative prediction algorithms were difficult to develop in those settings. Two clinical scenarios are discussed where PPM was explored through novel econometric methods. Related discussions around exploring PPM processes, multi-dimensionality of outcomes, and a balanced agenda for future research on personalization follow.

Suggested Citation

  • Basu Anirban, 2013. "Personalized Medicine in the Context of Comparative Effectiveness Research," Forum for Health Economics & Policy, De Gruyter, vol. 16(2), pages 73-86, June.
    • Handle: RePEc:bpj:fhecpo:v:16:y:2013:i:2:p:107-120:n:1
    DOI: 10.1515/fhep-2013-0009
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/fhep-2013-0009
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/fhep-2013-0009?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Basu, Anirban & Jena, Anupam B. & Philipson, Tomas J., 2011. "The impact of comparative effectiveness research on health and health care spending," Journal of Health Economics, Elsevier, vol. 30(4), pages 695-706, July.
    2. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    3. Heckman, James, 2001. "Accounting for Heterogeneity, Diversity and General Equilibrium in Evaluating Social Programmes," Economic Journal, Royal Economic Society, vol. 111(475), pages 654-699, November.
    4. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: an application to treatments of breast cancer patients," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1133-1157.
    5. Anirban Basu, 2012. "Estimating Person-Centered Treatment (PeT) Effects Using Instrumental Variables," NBER Working Papers 18056, National Bureau of Economic Research, Inc.
    6. Hsiao,Cheng & Morimune,Kimio & Powell,James L. (ed.), 2001. "Nonlinear Statistical Modeling," Cambridge Books, Cambridge University Press, number 9780521662468, January.
    7. Anirban Basu, 2009. "Individualization at the Heart of Comparative Effectiveness Research: The Time for i-CER Has Come," Medical Decision Making, , vol. 29(6), pages 9-11, November.
    8. Anirban Basu & David Meltzer, 2007. "Value of Information on Preference Heterogeneity and Individualized Care," Medical Decision Making, , vol. 27(2), pages 112-127, March.
    9. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: An application in breast cancer patients," Health, Econometrics and Data Group (HEDG) Working Papers 07/07, HEDG, c/o Department of Economics, University of York.
    10. Basu, Anirban, 2011. "Economics of individualization in comparative effectiveness research and a basis for a patient-centered health care," Journal of Health Economics, Elsevier, vol. 30(3), pages 549-559, May.
    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. Anirban Basu & Josh J. Carlson & David L. Veenstra, 2016. "A Framework for Prioritizing Research Investments in Precision Medicine," Medical Decision Making, , vol. 36(5), pages 567-580, July.

    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. Anirban Basu, 2012. "Estimating Person-Centered Treatment (PeT) Effects Using Instrumental Variables," NBER Working Papers 18056, National Bureau of Economic Research, Inc.
    2. H. Evans & A. Basu, 2011. "Exploring comparative effect heterogeneity with instrumental variables: prehospital intubation and mortality," Health, Econometrics and Data Group (HEDG) Working Papers 11/26, HEDG, c/o Department of Economics, University of York.
    3. Anirban Basu & Anupam B. Jena & Dana P. Goldman & Tomas J. Philipson & Robert Dubois, 2014. "Heterogeneity In Action: The Role Of Passive Personalization In Comparative Effectiveness Research," Health Economics, John Wiley & Sons, Ltd., vol. 23(3), pages 359-373, March.
    4. Basu, Anirban, 2015. "Welfare implications of learning through solicitation versus diversification in health care," Journal of Health Economics, Elsevier, vol. 42(C), pages 165-173.
    5. Cornelissen, Thomas & Dustmann, Christian & Raute, Anna & Schönberg, Uta, 2016. "From LATE to MTE: Alternative methods for the evaluation of policy interventions," Labour Economics, Elsevier, vol. 41(C), pages 47-60.
    6. Martin Nybom, 2017. "The Distribution of Lifetime Earnings Returns to College," Journal of Labor Economics, University of Chicago Press, vol. 35(4), pages 903-952.
    7. Olivier De Groote & Koen Declercq, 2021. "Tracking and specialization of high schools: Heterogeneous effects of school choice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 898-916, November.
    8. Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.
    9. Elisa Gerten & Michael Beckmann & Elisa Gerten & Matthias Kräkel, 2022. "Information and Communication Technology, Hierarchy, and Job Design," ECONtribute Discussion Papers Series 189, University of Bonn and University of Cologne, Germany.
    10. Gelo, Dambala & Dikgang, Johane, 2019. "Collective action and heterogeneous welfare effects: Evidence from Ethiopian villages," World Development Perspectives, Elsevier, vol. 16(C).
    11. William N. Evans & Craig Garthwaite, 2012. "Estimating Heterogeneity in the Benefits of Medical Treatment Intensity," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 635-649, August.
    12. Hoshino, Tadao & Yanagi, Takahide, 2023. "Treatment effect models with strategic interaction in treatment decisions," Journal of Econometrics, Elsevier, vol. 236(2).
    13. Lina Zhang & David T. Frazier & D. S. Poskitt & Xueyan Zhao, 2020. "Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects," Papers 2009.02642, arXiv.org, revised Sep 2022.
    14. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.
    15. Mullahy, John, 2018. "Individual results may vary: Inequality-probability bounds for some health-outcome treatment effects," Journal of Health Economics, Elsevier, vol. 61(C), pages 151-162.
    16. Daniel A Kamhöfer & Hendrik Schmitz & Matthias Westphal, 2019. "Heterogeneity in Marginal Non-Monetary Returns to Higher Education," Journal of the European Economic Association, European Economic Association, vol. 17(1), pages 205-244.
    17. Domenico Depalo, 2020. "Explaining the causal effect of adherence to medication on cholesterol through the marginal patient," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 110-126, October.
    18. Basu, Anirban, 2011. "Economics of individualization in comparative effectiveness research and a basis for a patient-centered health care," Journal of Health Economics, Elsevier, vol. 30(3), pages 549-559, May.
    19. Gelo, Dambala, 2020. "Forest commons, vertical integration and smallholder’s saving and investment responses: Evidence from a quasi-experiment," World Development, Elsevier, vol. 132(C).
    20. Walters, Lurleen M. & Emerson, Robert D. & Iwai, Nobuyuki, 2008. "Proposed Immigration Policy Reform & Farm Labor Market Outcomes," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6285, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

    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:bpj:fhecpo:v:16:y:2013:i:2:p:107-120:n:1. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.