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Therapeutic substitutions in the midst of new technology diffusion: The case of treatment for localized prostate cancer

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  • Shen, Chan
  • Tina Shih, Ya-Chen

Abstract

Robotic surgical systems have become increasingly popular worldwide. Robotic assisted radical prostatectomies have been widely adopted in the treatment of localized prostate cancer, replacing the conventional open surgeries. However, it is not clear whether this was achieved by substitution within the same treatment type (i.e., replacing open surgeries with robotic-assisted surgeries) or substitution across treatment types (i.e., expanding the proportion of patients receiving surgery while crowding out other forms of treatment for localized prostate cancer). Given the large number of patients undergoing these procedures each year, it is important to study the impact of the fast diffusion of robotic surgical systems on the overall treatment pattern of localized prostate cancer. We addressed this question using state-level cancer epidemiology data (256 observations) extracted from 2002 to 2010 National Cancer Database, and supply-side variables (e.g. density of robotic surgical systems, urologists) obtained from Area Resource File as well as investor presentations posted at the website of the manufacturer of robotic surgical systems. Recognizing that the purchase decision of robotic systems is potentially endogenous, we used an optimal instrumental variables panel estimation method to examine the impact while taking into account of the panel structure and the potential endogeneity of the density of robotic surgical systems and its quadratic term. We found that the density of robotic systems at state-level had a significantly positive impact on the rate of surgery and a significantly negative impact on the rate of radiation therapy. Further, our age-stratified analysis showed that the increase in surgery rate was most pronounced in the younger population. In conclusion, our findings suggest that part of the increase in the rate of surgery was driven by substitution across treatment types with a large proportion originating from the younger population.

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  • Shen, Chan & Tina Shih, Ya-Chen, 2016. "Therapeutic substitutions in the midst of new technology diffusion: The case of treatment for localized prostate cancer," Social Science & Medicine, Elsevier, vol. 151(C), pages 110-120.
    • Handle: RePEc:eee:socmed:v:151:y:2016:i:c:p:110-120
    DOI: 10.1016/j.socscimed.2016.01.016
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    References listed on IDEAS

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    1. Mick P. Couper & Eleanor Singer & Carrie A. Levin & Floyd J. Fowler Jr. & Angela Fagerlin & Brian J. Zikmund-Fisher, 2010. "Use of the Internet and Ratings of Information Sources for Medical Decisions: Results from the DECISIONS Survey," Medical Decision Making, , vol. 30(5_suppl), pages 106-114, September.
    2. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    3. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    4. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
    5. Bronwyn H. Hall & Beethika Khan, 2003. "Adoption of New Technology," NBER Working Papers 9730, National Bureau of Economic Research, Inc.
    6. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    7. Newey, Whitney K, 1990. "Efficient Instrumental Variables Estimation of Nonlinear Models," Econometrica, Econometric Society, vol. 58(4), pages 809-837, July.
    8. Chamberlain, Gary, 1987. "Asymptotic efficiency in estimation with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 34(3), pages 305-334, March.
    9. Amemiya, Takeshi, 1977. "The Maximum Likelihood and the Nonlinear Three-Stage Least Squares Estimator in the General Nonlinear Simultaneous Equation Model," Econometrica, Econometric Society, vol. 45(4), pages 955-968, May.
    10. Koenker, Roger, 1981. "A note on studentizing a test for heteroscedasticity," Journal of Econometrics, Elsevier, vol. 17(1), pages 107-112, September.
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    2. Horn, Danea & Sacarny, Adam & Zhou, Annetta, 2022. "Technology adoption and market allocation: The case of robotic surgery," Journal of Health Economics, Elsevier, vol. 86(C).

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