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Instrumental variable method for time-to-event data using a pseudo-observation approach

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  • Maiken I. S. Kjaersgaard
  • Erik T. Parner

Abstract

type="main" xml:lang="en"> Observational studies are often in peril of unmeasured confounding. Instrumental variable analysis is a method for controlling for unmeasured confounding. As yet, theory on instrumental variable analysis of censored time-to-event data is scarce. We propose a pseudo-observation approach to instrumental variable analysis of the survival function, the restricted mean, and the cumulative incidence function in competing risks with right-censored data using generalized method of moments estimation. For the purpose of illustrating our proposed method, we study antidepressant exposure in pregnancy and risk of autism spectrum disorder in offspring, and the performance of the method is assessed through simulation studies.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:2:p:463-472
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    Cited by:

    1. Jad Beyhum, 2021. "Two-stage least squares with a randomly right censored outcome," Papers 2110.05107, arXiv.org.
    2. Jad Beyhum & Jean-Pierre Florens & Ingrid Van Keilegom, 2021. "A nonparametric instrumental approach to endogeneity in competing risks models," Papers 2105.00946, arXiv.org.
    3. 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.

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