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Heterogeneity In Action: The Role Of Passive Personalization In Comparative Effectiveness Research

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  • Anirban Basu
  • Anupam B. Jena
  • Dana P. Goldman
  • Tomas J. Philipson
  • Robert Dubois

Abstract

Despite the goal of comparative effectiveness research (CER) to inform patient‐centered care, most studies fail to account for the patient‐centeredness of care that already exist in practice, which we denote as passive personalization (PP). Because CER studies describe the average effectiveness of treatments rather than heterogeneity in how individual patients respond to therapies, clinical or coverage policies that respond to CER results may undermine PP in clinical practice and generate worse outcomes. We study this phenomenon empirically in the context of use of antipsychotic drugs in Medicaid patients with schizophrenia using novel instrumental variable methods. We find strong support for PP in clinical practice and demonstrate that the average effects from a CER study cannot be replicated in practice because of the presence of PP. In contrast, providing physicians with evidence to further personalize treatment can produce significant benefits. Copyright © 2013 John Wiley & Sons, Ltd.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:hlthec:v:23:y:2014:i:3:p:359-373
    DOI: 10.1002/hec.2996
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    References listed on IDEAS

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    7. 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.
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    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 to treatments of breast cancer patients," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1133-1157.
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    Cited by:

    1. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.
    2. Kristopher J. Hult, 2017. "Measuring the Potential Health Impact of Personalized Medicine: Evidence from MS Treatments," NBER Working Papers 23900, National Bureau of Economic Research, Inc.
    3. Kristopher J. Hult, 2018. "Measuring the Potential Health Impact of Personalized Medicine: Evidence from Multiple Sclerosis Treatments," NBER Chapters, in: Economic Dimensions of Personalized and Precision Medicine, pages 185-216, National Bureau of Economic Research, Inc.
    4. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
    5. David D. Kim & Anirban Basu, 2017. "New Metrics for Economic Evaluation in the Presence of Heterogeneity: Focusing on Evaluating Policy Alternatives Rather than Treatment Alternatives," Medical Decision Making, , vol. 37(8), pages 930-941, November.
    6. Anirban Basu, 2018. "Comment: Manski's views on patient care under uncertainty," Health Economics, John Wiley & Sons, Ltd., vol. 27(10), pages 1422-1424, October.
    7. Jason Abaluck & Leila Agha & David C. Chan Jr & Daniel Singer & Diana Zhu, 2020. "Fixing Misallocation with Guidelines: Awareness vs. Adherence," NBER Working Papers 27467, National Bureau of Economic Research, Inc.

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