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Nonparametric estimation of the cumulative incidence function under outcome misclassification using external

Jessie K Edwards1, Giorgos Bakoyannis2, Constantin T Yiannoutsos2

  • 1Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

Statistics in Medicine
|October 25, 2019
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Summary

This study addresses outcome misclassification in competing risks research. It introduces a method using external validation data to correct for differential misclassification, improving cumulative incidence estimates.

Keywords:
competing riskscumulative incidenceexternal validation datamisclassification

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Sciences Research

Background:

  • Outcome misclassification is a significant challenge in health sciences research, potentially biasing cumulative incidence function (CIF) estimates in competing risks settings.
  • Existing methods address nondifferential misclassification with known probabilities, but differential misclassification remains a challenge.

Purpose of the Study:

  • To extend existing methods for estimating CIFs to account for differential outcome misclassification using external validation data.
  • To develop a robust statistical approach that incorporates uncertainty from estimated misclassification probabilities into confidence intervals.

Main Methods:

  • Utilized misclassification probabilities estimated from external validation data to adjust CIFs for differential misclassification.
  • Implemented a bootstrap resampling approach for both main and validation study data to ensure accurate confidence interval coverage.
  • Proposed a uniformly consistent estimator for CIFs under differential misclassification.

Main Results:

  • The proposed estimator demonstrated uniform consistency.
  • Simulation studies confirmed good performance of the estimator and standard error estimation in finite samples.
  • The methodology was successfully applied to estimate competing risks of death and disengagement from HIV care in sub-Saharan Africa.

Conclusions:

  • The developed method effectively corrects for differential outcome misclassification in competing risks analysis.
  • The bootstrap approach ensures reliable confidence intervals by accounting for uncertainty in misclassification probabilities.
  • This research provides a valuable tool for accurate epidemiological analysis in settings with underreported outcomes, such as HIV care in sub-Saharan Africa.