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ESTIMATING PERSON‐CENTERED TREATMENT (PeT) EFFECTS USING INSTRUMENTAL VARIABLES: AN APPLICATION TO EVALUATING PROSTATE CANCER TREATMENTS

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  • Anirban Basu

Abstract

SUMMARY This paper builds on the methods of local instrumental variables developed by Heckman and Vytlacil (1999, 2001, 2005) to estimate person‐centered treatment (PeT) effects that are conditioned on the person's observed characteristics and averaged over the potential conditional distribution of unobserved characteristics that lead them to their observed treatment choices. PeT effects are more individualized than conditional treatment effects from a randomized setting with the same observed characteristics. PeT effects can be easily aggregated to construct any of the mean treatment effect parameters and, more importantly, are well suited to comprehend individual‐level treatment effect heterogeneity. The paper presents the theory behind PeT effects, and applies it to study the variation in individual‐level comparative effects of prostate cancer treatments on overall survival and costs. Copyright © 2013 John Wiley & Sons, Ltd.

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  • Anirban Basu, 2014. "ESTIMATING PERSON‐CENTERED TREATMENT (PeT) EFFECTS USING INSTRUMENTAL VARIABLES: AN APPLICATION TO EVALUATING PROSTATE CANCER TREATMENTS," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(4), pages 671-691, June.
    • Handle: RePEc:wly:japmet:v:29:y:2014:i:4:p:671-691
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. David Glynn & John Giardina & Julia Hatamyar & Ankur Pandya & Marta Soares & Noemi Kreif, 2024. "Integrating decision modeling and machine learning to inform treatment stratification," Health Economics, John Wiley & Sons, Ltd., vol. 33(8), pages 1772-1792, August.
    5. Basu, Anirban & Jones, Andrew M. & Dias, Pedro Rosa, 2018. "Heterogeneity in the impact of type of schooling on adult health and lifestyle," Journal of Health Economics, Elsevier, vol. 57(C), pages 1-14.
    6. Silvia Moler‐Zapata & Richard Grieve & Anirban Basu & Stephen O’Neill, 2023. "How does a local instrumental variable method perform across settings with instruments of differing strengths? A simulation study and an evaluation of emergency surgery," Health Economics, John Wiley & Sons, Ltd., vol. 32(9), pages 2113-2126, September.
    7. Carl Bonander & Mikael Svensson, 2021. "Using causal forests to assess heterogeneity in cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 30(8), pages 1818-1832, August.
    8. Emely Ek Blæhr & Rikke Søgaard, 2021. "Instrumental variable‐based assessment of the effect of psychotherapy on suicide attempts, health, and economic outcomes in schizophrenia," Health Economics, John Wiley & Sons, Ltd., vol. 30(4), pages 903-914, April.
    9. Anirban Basu & Andrew M. Jones & Pedro Rosa Dias, 2014. "The Roles of Cognitive and Non-Cognitive Skills in Moderating the Effects of Mixed-Ability Schools on Long-Term Health," NBER Working Papers 20811, National Bureau of Economic Research, Inc.
    10. Gemma E. Shields & Paul Clarkson & Ash Bullement & Warren Stevens & Mark Wilberforce & Tracey Farragher & Arpana Verma & Linda M. Davies, 2024. "Advances in Addressing Patient Heterogeneity in Economic Evaluation: A Review of the Methods Literature," PharmacoEconomics, Springer, vol. 42(7), pages 737-749, July.

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