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A nonparametric instrumental approach to confounding in competing risks models

Author

Listed:
  • Jad Beyhum

    (ORSTAT, KU Leuven)

  • Jean-Pierre Florens

    (Université Toulouse Capitole)

  • Ingrid Keilegom

    (ORSTAT, KU Leuven)

Abstract

This paper discusses nonparametric identification and estimation of the causal effect of a treatment in the presence of confounding, competing risks and random right-censoring. Our identification strategy is based on an instrumental variable. We show that the competing risks model generates a nonparametric quantile instrumental regression problem. Quantile treatment effects on the subdistribution function can be recovered from the regression function. A distinguishing feature of the model is that censoring and competing risks prevent identification at some quantiles. We characterize the set of quantiles for which exact identification is possible and give partial identification results for other quantiles. We outline an estimation procedure and discuss its properties. The finite sample performance of the estimator is evaluated through simulations. We apply the proposed method to the Health Insurance Plan of Greater New York experiment.

Suggested Citation

  • Jad Beyhum & Jean-Pierre Florens & Ingrid Keilegom, 2023. "A nonparametric instrumental approach to confounding in competing risks models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 709-734, October.
    • Handle: RePEc:spr:lifeda:v:29:y:2023:i:4:d:10.1007_s10985-023-09599-3
    DOI: 10.1007/s10985-023-09599-3
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    References listed on IDEAS

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    1. Kaspar Wüthrich, 2020. "A Comparison of Two Quantile Models With Endogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 443-456, April.
    2. Xiaohong Chen & Oliver Linton & Ingrid Van Keilegom, 2003. "Estimation of Semiparametric Models when the Criterion Function Is Not Smooth," Econometrica, Econometric Society, vol. 71(5), pages 1591-1608, September.
    3. Maiken I. S. Kjaersgaard & Erik T. Parner, 2016. "Instrumental variable method for time-to-event data using a pseudo-observation approach," Biometrics, The International Biometric Society, vol. 72(2), pages 463-472, June.
    4. Sonja A. Swanson & Miguel A. Hernán & Matthew Miller & James M. Robins & Thomas S. Richardson, 2018. "Partial Identification of the Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, Treatments, and Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 933-947, April.
    5. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
    6. N W Deresa & I Van Keilegom, 2021. "On semiparametric modelling, estimation and inference for survival data subject to dependent censoring [Identifiability of the multinormal and other distributions under competing risks model]," Biometrika, Biometrika Trust, vol. 108(4), pages 965-979.
    7. Brigham R. Frandsen, 2015. "Treatment Effects With Censoring and Endogeneity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1745-1752, December.
    8. Rebecca A. Betensky & David A. Schoenfeld, 2001. "Nonparametric Estimation in a Cure Model with Random Cure Times," Biometrics, The International Biometric Society, vol. 57(1), pages 282-286, March.
    9. Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
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