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An R-Based Landscape Validation of a Competing Risk Model
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A nonparametric instrumental approach to confounding in competing risks models.

Jad Beyhum1, Jean-Pierre Florens2, Ingrid Van Keilegom3

  • 1ORSTAT, KU Leuven, Naamsestraat 69, 3000, Leuven, Belgium. jad.beyhum@gmail.com.

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Summary

This study introduces a new method for causal effect estimation using instrumental variables, addressing confounding, competing risks, and censoring. It enables quantile treatment effect recovery, with partial identification for unidentifiable quantiles.

Keywords:
Competing risksConfoundingInstrumental variable

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Area of Science:

  • Biostatistics
  • Econometrics
  • Epidemiology

Background:

  • Causal inference is challenging with confounding, competing risks, and censoring.
  • Existing methods may not fully address these complexities in treatment effect estimation.

Purpose of the Study:

  • To develop a nonparametric method for identifying and estimating causal treatment effects under complex data conditions.
  • To recover quantile treatment effects on the subdistribution function.

Main Methods:

  • Utilizing an instrumental variable strategy for identification.
  • Formulating the problem as a nonparametric quantile instrumental regression within a competing risks framework.
  • Characterizing identifiable quantiles and providing partial identification for others.

Main Results:

  • Demonstrated the recovery of quantile treatment effects from the regression function.
  • Identified specific quantiles where exact identification is possible.
  • Provided partial identification results for quantiles affected by censoring and competing risks.

Conclusions:

  • The proposed instrumental variable approach offers a robust method for causal inference in the presence of confounding, competing risks, and censoring.
  • The method allows for the estimation of quantile treatment effects, with clear characterization of identification limitations.