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Specification Issues in a Big Data Context: Controlling for the Endogeneity of Consumer and Provider Behaviours in Healthcare Treatment Effects Models

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References

  1. Curtis L, Brown J, Platt R. Four health data networks illustrate the potential for a shared national multipurpose big-data network. Health Aff. 2014;33(7):1178–86.

    Article  Google Scholar 

  2. Etheredge L. Rapid learning: a breakthrough agenda. Health Aff. 2014;33(7):1155–61.

    Article  Google Scholar 

  3. Wallace P, Shah N, Dennon T, Bleicher P, Crown W. Optum labs: building a novel node in the learning health care system. Health Aff. 2014;33(7):1187–94.

    Article  Google Scholar 

  4. Bishop CE, Ryan AM, Gilden DM, et al. Effect of an expenditure cap on low-income seniors’ drug use and spending in a state pharmacy assistance program. Health Serv Res. 2009;44(3):1010–28.

    Article  PubMed Central  PubMed  Google Scholar 

  5. Parente ST, Feldman R, Chen S. Effects of a consumer driven health plan on pharmaceutical spending and utilization. Health Serv Res. 2008;43(5):1542–56.

    Article  PubMed Central  PubMed  Google Scholar 

  6. Gilman BH, Kautter J. Impact of multitiered copayments on the use and cost of prescription drugs among Medicare beneficiaries. Health Serv Res. 2008;43(2):478–95.

    Article  PubMed Central  PubMed  Google Scholar 

  7. Klepser DG, Huether JR, Handke LJ, Williams CE. Effect on drug utilization and expenditures of a cost-share change from copayment to coinsurance. J Manag Care Pharm. 2007;13(9):765–77.

    PubMed  Google Scholar 

  8. Shrank WH, Stedman M, Ettner SL, et al. Patient, physician, pharmacy, and pharmacy benefit design factors related to generic medication use. J Gen Int Med. 2007;22(9):1298–304.

    Article  Google Scholar 

  9. Huskamp HA, Deverka PA, Epstein AM, et al. The effect of incentive-based formularies on prescription-drug utilization and spending. N Engl J Med. 2003;349(23):2224–32.

    Article  CAS  PubMed  Google Scholar 

  10. Chandra A, Cutler D, Song Z. Who ordered that? The economics of treatment choices in medical care. Handbook Health Econ. 2012;2:46–93.

    Google Scholar 

  11. Hellerstein JK. The importance of the physician in the generic versus trade-name prescription decision. RAND J Econ. 1998;29:108–36.

    Article  CAS  PubMed  Google Scholar 

  12. Domencich T, McFadden D. Urban travel demand: a behavioral analysis. Oxford: North Holland Publishing Company; 1975.

    Google Scholar 

  13. Heckman J. The common structure of statistical models of truncation, sample selection, and limited dependent variables and a simple estimator for such models. In: Berg SV. Annals of economic and social measurement. London; National Bureau of Economic Research; 1976.

  14. Brookhart M, Rassen J, Schneeweiss S. Instrumental variable methods in comparative safety and effectiveness research. Pharmacoepidemiol Drug Saf. 2010;19(6):537–54.

    Article  PubMed Central  PubMed  Google Scholar 

  15. Terza J, Basu A, Rathouz P. Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling. J Health Econ. 2008;27:531–43.

    Article  PubMed Central  PubMed  Google Scholar 

  16. Hausman J. Specification tests in econometrics. Econometrica. 1978;46:1251–71.

    Article  Google Scholar 

  17. Bound J, Jaeger DA, Baker RM. Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. J Am Stat Assoc. 1995;90(430):443–50.

    Google Scholar 

  18. Crown W, Henk H, VanNess D. Endogenous treatment selection: how bias in instrumental variables estimators is affected by instrument strength, instrument contamination, and sample size. Value Health. 2011;14:1078–84.

    Article  PubMed  Google Scholar 

  19. Murray M. Avoiding invalid instruments and coping with weak Instruments. J Econ Perspect. 2007;20(4):111–32.

    Article  Google Scholar 

  20. Basu A, Heckman J, Navarro-Lozano S, Urzua S. Use of instrumental variables in the presence of heterogeneity and self-selection: an application to treatments of breast cancer patients. Health Econ. 2007;16:1133–57.

    Article  PubMed  Google Scholar 

  21. Crown W, Chang J, Olson M, et al. Can statistical linkage of missing variables reduce bias in treatment effect estimates in comparative effectiveness research studies? J Comp Eff Res. 2015;4(5):455–63.

    Article  PubMed  Google Scholar 

  22. Becker G. A theory of the allocation of time. Econ J. 1965;75:493–517.

  23. Grossman M. On the concept of health capital and the demand for health. J Political Econ. 1972;80(2):223–55.

    Article  Google Scholar 

  24. Brown J, Duggan M, Kuziemko I, Woolston W. How does risk selection respond to risk adjustment? New evidence from the Medicare Advantage Program. Am Econ Rev. 2014;104(10):3335–64.

    Article  Google Scholar 

  25. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference and prediction. New York: Springer; 2009.

    Book  Google Scholar 

  26. Crown W. Potential applications of machine learning in health outcomes research and some statistical cautions. Value Health. 2015;18(2):137–40.

    Article  PubMed  Google Scholar 

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Correspondence to William H. Crown.

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Crown, W.H. Specification Issues in a Big Data Context: Controlling for the Endogeneity of Consumer and Provider Behaviours in Healthcare Treatment Effects Models. PharmacoEconomics 34, 95–100 (2016). https://doi.org/10.1007/s40273-015-0362-z

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