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A pseudo-likelihood method for estimating misclassification probabilities in competing-risks settings when true-event

Philani B Mpofu1, Giorgos Bakoyannis1, Constantin T Yiannoutsos1

  • 1Department of Biostatistics, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, IN, USA.

Biometrical Journal. Biometrische Zeitschrift
|June 11, 2020
PubMed
Summary

This study introduces a new method to accurately estimate outcome misclassification probabilities in studies, even when data is missing. The approach is computationally efficient and works under a missing at random assumption, improving bias correction for incidence and prevalence.

Keywords:
competing risksinternal validationmisclassificationmissing datapseudo-likelihood

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Research Methods

Background:

  • Outcome misclassification is common in binary-outcome studies, biasing key epidemiological measures like incidence and prevalence.
  • Existing methods to correct for misclassification often rely on strong assumptions (e.g., missing completely at random) or are computationally intensive.
  • Internal validation using a gold-standard procedure introduces a missing data problem for true outcomes.

Purpose of the Study:

  • To develop a computationally efficient and easy-to-implement method for estimating misclassification probabilities.
  • To address limitations of existing methods by using a missing at random (MAR) assumption.
  • To provide a robust approach for bias correction in studies with internal validation samples.

Main Methods:

  • Proposed a pseudo-likelihood estimator for misclassification probabilities.
  • The method is designed for studies with internal validation samples and assumes data is missing at random (MAR).
  • Derived consistency, asymptotic distributional properties, and a closed-form variance estimator.

Main Results:

  • The proposed pseudo-likelihood estimator is computationally efficient and easy to implement.
  • Demonstrated consistency and derived asymptotic distributional properties for the estimator.
  • Evaluated finite-sample performance through simulations and real-world data application.

Conclusions:

  • The novel estimator effectively addresses outcome misclassification under MAR assumptions.
  • The method provides a practical solution for bias adjustment in epidemiological studies, particularly those with competing risks.
  • Facilitates more accurate estimation of epidemiological measures by correcting for misclassification bias.