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Extended Instrumental Variables Estimation for Overall Effects

Author

Listed:
  • Joffe Marshall M

    (University of Pennsylvania)

  • Small Dylan

    (University of Pennsylvania)

  • Ten Have Thomas

    (University of Pennsylvania)

  • Brunelli Steve

    (University of Pennsylvania)

  • Feldman Harold I

    (University of Pennsylvania)

Abstract

We consider a method for extending instrumental variables methods in order to estimate the overall effect of a treatment or exposure. The approach is designed for settings in which the instrument influences both the treatment of interest and a secondary treatment also influenced by the primary treatment. We demonstrate that, while instrumental variables methods may be used to estimate the joint effects of the primary and secondary treatments, they cannot by themselves be used to estimate the overall effect of the primary treatment. However, instrumental variables methods may be used in conjunction with approaches for estimating the effect of the primary on the secondary treatment to estimate the overall effect of the primary treatment. We consider extending the proposed methods to deal with confounding of the effect of the instrument, mediation of the effect of the instrument by other variables, failure-time outcomes, and time-varying secondary treatments. We motivate our discussion by considering estimation of the overall effect of the type of vascular access among hemodialysis patients.

Suggested Citation

  • Joffe Marshall M & Small Dylan & Ten Have Thomas & Brunelli Steve & Feldman Harold I, 2008. "Extended Instrumental Variables Estimation for Overall Effects," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-22, April.
    • Handle: RePEc:bpj:ijbist:v:4:y:2008:i:1:n:4
    DOI: 10.2202/1557-4679.1082
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    References listed on IDEAS

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    1. James Robins & Andrea Rotnitzky, 2004. "Estimation of treatment effects in randomised trials with non-compliance and a dichotomous outcome using structural mean models," Biometrika, Biometrika Trust, vol. 91(4), pages 763-783, December.
    2. Thomas R. Ten Have & Marshall M. Joffe & Kevin G. Lynch & Gregory K. Brown & Stephen A. Maisto & Aaron T. Beck, 2007. "Causal Mediation Analyses with Rank Preserving Models," Biometrics, The International Biometric Society, vol. 63(3), pages 926-934, September.
    3. K. Newey, Whitney, 1985. "Generalized method of moments specification testing," Journal of Econometrics, Elsevier, vol. 29(3), pages 229-256, September.
    4. Ten Have, Thomas R. & Elliott, Michael R. & Joffe, Marshall & Zanutto, Elaine & Datto, Catherine, 2004. "Causal Models for Randomized Physician Encouragement Trials in Treating Primary Care Depression," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 16-25, January.
    5. Small, Dylan S., 2007. "Sensitivity Analysis for Instrumental Variables Regression With Overidentifying Restrictions," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1049-1058, September.
    6. McClellan, Mark & Newhouse, Joseph P., 1997. "The marginal cost-effectiveness of medical technology: A panel instrumental-variables approach," Journal of Econometrics, Elsevier, vol. 77(1), pages 39-64, March.
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    Cited by:

    1. Karim Chalak & Halbert White, 2011. "Viewpoint: An extended class of instrumental variables for the estimation of causal effects," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 44(1), pages 1-51, February.
    2. Markus Frölich & Martin Huber, 2017. "Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1645-1666, November.

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    Keywords

    instrumental variables; causal inference;

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