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Bayesian inference for unidirectional misclassification of a binary response trait.

Michelle Xia1, Paul Gustafson2

  • 1Division of Statistics, Northern Illinois University, DeKalb, IL 60115, USA.

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Summary
This summary is machine-generated.

This study shows that unidirectional misclassification in binary traits can be identified, except with single binary covariates. The proposed method helps adjust for misclassification or analyze its impact in statistical models.

Keywords:
Bayesian inferenceMarkov chain Monte Carlopartial identificationunidirectional misclassification

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Binary response variables can suffer from unidirectional misclassification due to social desirability or financial costs.
  • Model identification issues often hinder the analysis of misclassified data.
  • Unidirectional misclassification can bias association estimates between traits and covariates.

Purpose of the Study:

  • To investigate the identifiability and efficacy of statistical inference for binary response variables with unidirectional misclassification.
  • To develop methods for adjusting or performing sensitivity analyses for misclassification.
  • To explore the theoretical and practical implications of misclassification in logistic and Poisson models.

Main Methods:

  • Theoretical analysis of parameter identifiability in models with unidirectional misclassification.
  • Investigation of weak identification issues in logistic models with quantitative covariates.
  • Extension to Poisson and zero-inflated Poisson models using a Poisson approximation.
  • Development of a data-driven adjustment method for fully identified models.

Main Results:

  • Key model parameters are identifiable, except in the specific case of a single binary covariate.
  • Logistic models with quantitative covariates may exhibit weak identification, impacting parameter estimation.
  • Stronger identification allows for more effective adjustment for misclassification.
  • Poisson and zero-inflated Poisson models demonstrate identifiability.

Conclusions:

  • The study provides a theoretical framework for understanding misclassification in binary traits.
  • The proposed methods offer practical solutions for adjusting for or analyzing the impact of unidirectional misclassification.
  • The findings are applicable to various statistical models, including logistic and Poisson regressions.