A nonparametric instrumental approach to confounding in competing risks models

Jad Beyhum (), Jean-Pierre Florens () and Ingrid Keilegom ()
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Jad Beyhum: ORSTAT, KU Leuven
Jean-Pierre Florens: Université Toulouse Capitole
Ingrid Keilegom: ORSTAT, KU Leuven

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2023, vol. 29, issue 4, No 2, 709-734

Abstract: 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.

Keywords: Competing risks; Confounding; Instrumental variable; MSC 62D20; MSC G2N02; MSC G2G08 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10985-023-09599-3

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