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Summary

This study extends measurement error models for human subjects, finding the two-stage method superior for estimating bias, especially with single measurements per person.

Keywords:
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Area of Science:

  • Biostatistics
  • Psychometrics
  • Measurement Theory

Background:

  • Standard statistical methods for agreement assessment often assume a constant latent trait.
  • This assumption is problematic when the 'individual' is a person, whose trait may change over time.

Purpose of the Study:

  • To extend the general measurement error model to accommodate a time-varying individual latent trait.
  • To evaluate statistical methods for estimating bias in measurement agreement when individual traits change.

Main Methods:

  • Investigated four settings: constant trait, variable trait without trend, linear trend, and approximate linear trend.
  • Assessed two methods: Generalized Least Squares (GLS) estimator (Sprent) and the two-stage method (Taffé).

Main Results:

  • The two-stage method generally outperformed the GLS estimator in estimating bias.
  • The two-stage method is applicable even with a single measurement per individual, unlike the GLS method.

Conclusions:

  • The two-stage method provides a more robust approach for assessing measurement agreement in human subjects.
  • This extended model and method are crucial for accurate bias estimation in longitudinal studies involving people.