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Use of instrumental variables in the presence of heterogeneity and self‐selection: an application to treatments of breast cancer patients

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  • Anirban Basu
  • James J. Heckman
  • Salvador Navarro‐Lozano
  • Sergio Urzua

Abstract

Instrumental variable (IV) methods are widely used in the health economics literature to adjust for hidden selection biases in observational studies when estimating treatment effects. Less attention has been paid in the applied literature to the proper use of IVs if treatment effects are heterogeneous across subjects and individuals select treatments based on expected idiosyncratic gains or losses from treatments. In this paper we compare conventional IV analysis with alternative approaches that use IVs to estimate treatment effects in models with response heterogeneity and self‐selection. Instead of interpreting IV estimates as the effect of treatment at an unknown margin of patients, we identify the marginal patients and we apply the method of local IVs to estimate the average treatment effect and the effect on the treated on 5‐year direct costs of breast‐conserving surgery and radiation therapy compared with mastectomy in breast cancer patients. We use a sample from the Outcomes and Preferences in Older Women, Nationwide Survey which is designed to be representative of all female Medicare beneficiaries (aged 67 or older) with newly diagnosed breast cancer between 1992 and 1994. Our results reveal some of the advantages and limitations of conventional and alternative IV methods in estimating mean treatment effect parameters. Copyright © 2007 John Wiley & Sons, Ltd.

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  • 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, November.
  • Handle: RePEc:wly:hlthec:v:16:y:2007:i:11:p:1133-1157:a
    DOI: 10.1002/hec.1291
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    Cited by:

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    3. Hoshino, Tadao & Yanagi, Takahide, 2023. "Treatment effect models with strategic interaction in treatment decisions," Journal of Econometrics, Elsevier, vol. 236(2).
    4. Philip Klein & Hedwig Blommestein & Maiwenn Al & Benedetta Pongiglione & Aleksandra Torbica & Saskia de Groot, 2022. "Real‐world evidence in health technology assessment of high‐risk medical devices: Fit for purpose?," Health Economics, John Wiley & Sons, Ltd., vol. 31(S1), pages 10-24, September.
    5. 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.
    6. Gerten, Elisa & Beckmann, Michael & Kräkel, Matthias, 2022. "Information and Communication Technology, Hierarchy, and Job Design," IZA Discussion Papers 15491, Institute of Labor Economics (IZA).
    7. 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.
    8. Lina Zhang & David T. Frazier & Don S. Poskitt & Xueyan Zhao, 2020. "Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects," Monash Econometrics and Business Statistics Working Papers 34/20, Monash University, Department of Econometrics and Business Statistics.
    9. 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.
    10. Andrew M. Jones, 2007. "Identification of treatment effects in Health Economics," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1127-1131, November.

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