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An exploratory instrumental variable analysis of the outcomes of localized breast cancer treatments in a medicare population

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  • Jack Hadley
  • Daniel Polsky
  • Jeanne S. Mandelblatt
  • Jean M. Mitchell
  • Jane C. Weeks
  • Qin Wang
  • Yi‐Ting Hwang
  • OPTIONS Research Team

Abstract

This study is motivated by the potential problem of using observational data to draw inferences about treatment outcomes when experimental data are not available. We compare two statistical approaches, ordinary least‐squares (OLS) and instrumental variables (IV) regression analysis, to estimate the outcomes (three‐year post‐treatment survival) of three treatments for early stage breast cancer in elderly women: mastectomy (MST), breast conserving surgery with radiation therapy (BCSRT), and breast conserving surgery only (BCSO). The primary data source was Medicare claims for a national random sample of 2907 women (age 67 or older) with localized breast cancer who were treated between 1992 and 1994. Contrary to randomized clinical trial (RCT) results, analysis with the observational data found highly significant differences in survival among the three treatment alternatives: 79.2% survival for BCSO, 85.3% for MST, and 93.0% for BCSRT. Using OLS to control for the effects of observable characteristics narrowed the estimated survival rate differences, which remained statistically significant. In contrast, the IV analysis estimated survival rate differences that were not significantly different from 0. However, the IV‐point estimates of the treatment effects were quantitatively larger than the OLS estimates, unstable, and not significantly different from the OLS results. In addition, both sets of estimates were in the same quantitative range as the RCT results. We conclude that unadjusted observational data on health outcomes of alternative treatments for localized breast cancer should not be used for cost‐effectiveness studies. Our comparisons suggest that whether one places greater confidence in the OLS or the IV results depends on at least three factors: (1) the extent of observable health information that can be used as controls in OLS estimation, (2) the outcomes of statistical tests of the validity of the instrumental variable method, and (3) the similarity of the OLS and IV estimates. In this particular analysis, the OLS estimates appear to be preferable because of the instability of the IV estimates. Copyright © 2002 John Wiley & Sons, Ltd.

Suggested Citation

  • Jack Hadley & Daniel Polsky & Jeanne S. Mandelblatt & Jean M. Mitchell & Jane C. Weeks & Qin Wang & Yi‐Ting Hwang & OPTIONS Research Team, 2003. "An exploratory instrumental variable analysis of the outcomes of localized breast cancer treatments in a medicare population," Health Economics, John Wiley & Sons, Ltd., vol. 12(3), pages 171-186, March.
    • Handle: RePEc:wly:hlthec:v:12:y:2003:i:3:p:171-186
    DOI: 10.1002/hec.710
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    References listed on IDEAS

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    1. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    2. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
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    1. Budi Hidayat & Subhash Pokhrel, 2009. "The Selection of an Appropriate Count Data Model for Modelling Health Insurance and Health Care Demand: Case of Indonesia," IJERPH, MDPI, vol. 7(1), pages 1-19, December.
    2. 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.
    3. Maffioli, Elisa M., 2021. "The political economy of health epidemics: Evidence from the Ebola outbreak," Journal of Development Economics, Elsevier, vol. 151(C).
    4. Daniel Polsky & Anirban Basu, 2012. "Selection Bias in Observational Data," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 46, Edward Elgar Publishing.
    5. Basu, A & Polsky, D & Manning, W G, 2008. "Use of propensity scores in non-linear response models: The case for health care expenditures," Health, Econometrics and Data Group (HEDG) Working Papers 08/11, HEDG, c/o Department of Economics, University of York.
    6. 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.
    7. Ertefaie Ashkan & Small Dylan & Flory James & Hennessy Sean, 2016. "Selection Bias When Using Instrumental Variable Methods to Compare Two Treatments But More Than Two Treatments Are Available," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 219-232, May.
    8. Pedro Saramago & Karl Claxton & Nicky J. Welton & Marta Soares, 2020. "Bayesian econometric modelling of observational data for cost‐effectiveness analysis: establishing the value of negative pressure wound therapy in the healing of open surgical wounds," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1575-1593, October.
    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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