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Exploring comparative effect heterogeneity with instrumental variables: prehospital intubation and mortality

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  • Evans, H.;
  • Basu, A;

Abstract

We highlight the role of local instrumental variable (LIV) methods in exploring treatment effect heterogeneity using an empirical example of evaluating the use versus non-use of prehospital intubation (PHI) in patients with traumatic injury. We find evidence that effect of PHI on inpatient mortality vary over levels of unobserved confounders giving rise to a phenomenon known as essential heterogeneity. Under essential heterogeneity, the traditional instrumental variable (IV) method, when using a continuous IV, estimates an effect that is an arbitrary weighted average of the casual effects for marginal groups of patients whose PHI receipt are directly influenced by the IV levels. Instead, the LIV methods estimate the distribution of treatment effects for every margin and allows for predictable aggregation to recover estimates for meaningful treatment effect parameters such as the Average Treatment Effect (ATE) and the Effect on the Treated (TT). LIV methods also allow exploring heterogeneity in treatment effects over levels of observed confounders. In the PHI analysis, we estimate an ATE of 0.322 (se=0.16, p=0.04) and a TT of 0.046 (se=0.07, p=0.61). We find strong evidence of positive self-selection in practice, whereby patients who were most likely to be harmed by PHI were also less likely to receive PHI. However, the degree of positive self-selection mitigates in regions with higher rates of PHI use. We also explore factors associated with the prediction of significant harm by PHI. We provide clinical interpretation of results and discuss the importance of these methods in the context of comparative effectiveness research.

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Bibliographic Info

Paper provided by HEDG, c/o Department of Economics, University of York in its series Health, Econometrics and Data Group (HEDG) Working Papers with number 11/08.

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Date of creation: Jun 2011
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Handle: RePEc:yor:hectdg:11/08

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Postal: HEDG/HERC, Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom
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Web page: http://www.york.ac.uk/economics/postgrad/herc/hedg/
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Keywords: Instrumental variables; local IV methods; heterogeneity; prehospital intubation; mortality.;

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  1. 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.
  2. Abbring, Jaap H. & Heckman, James J., 2007. "Econometric Evaluation of Social Programs, Part III: Distributional Treatment Effects, Dynamic Treatment Effects, Dynamic Discrete Choice, and General Equilibrium Policy Evaluation," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 72 Elsevier.
  3. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, 05.
  4. Heckman, James J. & Urzua, Sergio & Vytlacil, Edward, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," IZA Discussion Papers 2320, Institute for the Study of Labor (IZA).
  5. Anirban Basu, 2011. "Economics of Individualization in Comparative Effectiveness Research and a Basis for a Patient-Centered Health Care," NBER Working Papers 16900, National Bureau of Economic Research, Inc.
  6. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 71 Elsevier.
  7. Terza, Joseph V. & Basu, Anirban & Rathouz, Paul J., 2008. "Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling," Journal of Health Economics, Elsevier, vol. 27(3), pages 531-543, May.
  8. Basu, Anirban & Jena, Anupam B. & Philipson, Tomas J., 2011. "The impact of comparative effectiveness research on health and health care spending," Journal of Health Economics, Elsevier, vol. 30(4), pages 695-706, July.
  9. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
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