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An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Assessing and quantifying inter-rater variation for dichotomous ratings using a Rasch model.

Jørgen Holm Petersen¹, Klaus Larsen, Svend Kreiner

¹Department of Biostatistics, University of Copenhagen, Øster Farimagsgade 5 entrance B, Postboks 2099, København, Denmark.

Statistical Methods in Medical Research

|December 24, 2010

Summary

We introduce a Rasch model to analyze agreement between raters using dichotomous ratings. This model quantifies rater bias and variation, offering a new method for assessing inter-rater reliability in medical assessments.

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Area of Science:

Statistics
Biostatistics
Medical Informatics

Background:

Analyzing agreement between multiple raters is crucial for reliable medical diagnoses.
Dichotomous ratings are common in clinical assessments but present unique statistical challenges.
Existing methods may not fully capture individual rater biases and variations.

Purpose of the Study:

To present a novel model-based approach for analyzing inter-rater agreement with dichotomous ratings.
To introduce a Rasch model incorporating rater-specific parameters to account for rater bias.
To develop a statistical test for rater bias and a measure for rater variation.

Main Methods:

A logistic regression model with random effects, specifically a Rasch model, was developed.
An exact score test was proposed to test the hypothesis of no rater bias.
The generalized McNemar's test was shown to be equivalent to the proposed score test.
Rater variation was quantified using the variation of rater odds ratios.

Main Results:

The proposed Rasch model effectively accounts for individual rater propensities (rater bias).
The exact score test provides a statistically rigorous method for detecting rater bias.
The generalized McNemar's test serves as an exact test for rater bias in this context.
Quantification of rater variation offers insights into the consistency of rating behavior.

Conclusions:

The Rasch model offers a robust framework for analyzing dichotomous inter-rater agreement.
The developed methods allow for the identification and quantification of rater bias and variation.
This approach enhances the reliability assessment of diagnostic tools like Umbilical artery Doppler velocimetry.