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Designs Combining Instrumental Variables with Case-Control: Estimating Principal Strata Causal Effects

Author

Listed:
  • Shinohara Russell T.

    (Johns Hopkins University)

  • Frangakis Constantine E.

    (Johns Hopkins University)

  • Platz Elizabeth

    (Johns Hopkins University)

  • Tsilidis Konstantinos

    (University of Ioannina)

Abstract

The instrumental variables framework is commonly used for the estimation of causal effects from cohort samples. However, the combination of instrumental variables with more efficient designs such as case-control sampling requires new methodological consideration. For example, as the use of Mendelian randomization studies is increasing and the cost of genotyping and gene expression data can be high, the analysis of data gathered from more cost-effective sampling designs is of prime interest. We show that the standard instrumental variables analysis does not appropriately estimate the causal effects of interest when the instrumental variables design is combined with the case-control design. We also propose a method that can estimate the causal effects in such combined designs. We illustrate the method with a study in oncology.

Suggested Citation

  • Shinohara Russell T. & Frangakis Constantine E. & Platz Elizabeth & Tsilidis Konstantinos, 2012. "Designs Combining Instrumental Variables with Case-Control: Estimating Principal Strata Causal Effects," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-21, January.
    • Handle: RePEc:bpj:ijbist:v:8:y:2012:i:1:n:2
    DOI: 10.2202/1557-4679.1355
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    References listed on IDEAS

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    1. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
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    3. Joffe Marshall, 2011. "Principal Stratification and Attribution Prohibition: Good Ideas Taken Too Far," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-22, September.
    4. Fan Li & Constantine E. Frangakis, 2006. "Polydesigns and Causal Inference," Biometrics, The International Biometric Society, vol. 62(2), pages 343-351, June.
    5. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    6. Frangakis, Constantine E. & Brookmeyer, Ronald S. & Varadhan, Ravi & Safaeian, Mahboobeh & Vlahov, David & Strathdee, Steffanie A., 2004. "Methodology for Evaluating a Partially Controlled Longitudinal Treatment Using Principal Stratification, With Application to a Needle Exchange Program," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 239-249, January.
    7. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
    8. Tan, Zhiqiang, 2006. "Regression and Weighting Methods for Causal Inference Using Instrumental Variables," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1607-1618, December.
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    Cited by:

    1. Dawid Philip & Didelez Vanessa, 2012. ""Imagine a Can Opener"--The Magic of Principal Stratum Analysis," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-12, July.

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