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Use of instrumental variables in the presence of heterogeneity and self-selection: An application in breast cancer patients

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

Abstract

Instrumental variables methods (IV) 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 instrumental variables if treatment effects are heterogeneous across subjects and individuals select treatments based on expected idiosyncratic gains or losses from treatments. In this paper, we analyze the role of conventional instrumental variable analysis and alternative approaches using instrumental variables for estimating treatment effects for models with treatment 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 instrumental variables to estimate the Average Treatment Effect (ATE) and the Effect on the Treated (TT) on 5-year direct costs of breast conserving surgery and radiation therapy compared to mastectomy in breast cancer patients. We use a sample from the Outcomes and Preferences in Older Women, Nationwide Survey (OPTIONS) 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.

Suggested Citation

  • 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.
  • Handle: RePEc:yor:hectdg:07/07
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. Basu Anirban, 2013. "Personalized Medicine in the Context of Comparative Effectiveness Research," Forum for Health Economics & Policy, De Gruyter, vol. 16(2), pages 107-120, June.
    3. John C. Ham & Daniela Iorio & Michelle Sovinsky, 2013. "Caught in the Bulimic Trap?," Journal of Human Resources, University of Wisconsin Press, vol. 48(3), pages 736-767.
    4. Kamhöfer, Daniel A. & Schmitz, Hendrik & Westphal, Matthias, 2015. "Heterogeneity in marginal non-monetary returns to higher education," Ruhr Economic Papers 591, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    5. M. Christopher Auld, 2012. "Using Observational Data to Identify the Causal Effects of Health-related Behaviour," Chapters,in: The Elgar Companion to Health Economics, Second Edition, chapter 4 Edward Elgar Publishing.
    6. 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.
    7. Ali, Shehzad & Cookson, Richard & Dusheiko, Mark, 2017. "Addressing care-seeking as well as insurance-seeking selection biases in estimating the impact of health insurance on out-of-pocket expenditure," Social Science & Medicine, Elsevier, vol. 177(C), pages 127-140.
    8. Raquel Fonseca Benito & Yuhui Zheng, 2011. "The Effect of Education on Health Cross-Country Evidence," Working Papers WR-864, RAND Corporation.
    9. Anirban Basu & Norma Coe & Cole G. Chapman, 2017. "Comparing 2SLS vs 2SRI for Binary Outcomes and Binary Exposures," NBER Working Papers 23840, National Bureau of Economic Research, Inc.
    10. Gelo, Dambala & Muchapondwa, Edwin & Koch, Steven F., 2016. "Decentralization, market integration and efficiency-equity trade-offs: Evidence from Joint Forest Management in Ethiopian villages," Journal of Forest Economics, Elsevier, vol. 22(C), pages 1-23.
    11. David Epstein & Dolores Jiménez-Rubio & Peter C. Smith & Marc Suhrcke, 2009. "Social determinants of health: an economic perspective," Health Economics, John Wiley & Sons, Ltd., vol. 18(5), pages 495-502.
    12. Radchenko, Natalia, 2014. "Heterogeneity in Informal Salaried Employment: Evidence from the Egyptian Labor Market Survey," World Development, Elsevier, vol. 62(C), pages 169-188.
    13. Feng, Y. & Devlin, N. & Bateman, A. & Zamora, B. & Parkin, D., 2016. "The Distribution of the EQ-5D-5L Index in Patient Populations," Research Papers 001756, Office of Health Economics.

    More about this item

    Keywords

    Self-selection; essential heterogeneity; instrumental variables; breast cancer; local instrumental variable method.;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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