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[Comparison of missing data handling methods for AC1 coefficient estimation].

Keke Li1, Lishan Xu1, Milai Yu1

  • 1Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510515, China.

Nan Fang Yi Ke Da Xue Xue Bao = Journal of Southern Medical University
|February 3, 2026
PubMed
Summary
This summary is machine-generated.

Choosing the right method to handle missing data is crucial for accurate AC1 coefficient estimation. Subject mode imputation is recommended for skewed prevalence under missing not at random (MNAR) conditions.

Keywords:
AC 1 coefficientagreement evaluationmissing datanominal ratings

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Missing data can significantly impact the accuracy of statistical estimates.
  • The AC1 coefficient is a measure of inter-rater reliability, sensitive to missing data.
  • Simulation studies are essential for evaluating data handling methods.

Purpose of the Study:

  • To compare the performance of four different missing data handling methods for AC1 coefficient estimation.
  • To identify the most suitable methods under various missing data mechanisms and prevalence conditions.
  • To provide guidance for researchers dealing with missing data in AC1 analyses.

Main Methods:

  • Monte Carlo simulations were employed to generate data with varying parameters.
  • Key parameters included number of raters, categories, sample size, disease prevalence, and missing proportion.
  • Four methods were evaluated: excluding zero ratings, excluding incomplete ratings, rater mode imputation, and subject mode imputation.
  • Bias and Mean Squared Error (MSE) were used as performance metrics.

Main Results:

  • Excluding zero ratings performed best when prevalence was balanced or data were missing completely at random (MCAR) or at random (MAR), with low bias and MSE below 30% missingness.
  • Subject mode imputation was superior for skewed prevalence under missing not at random (MNAR) conditions, yielding low bias and MSE.
  • Rater mode imputation consistently showed the poorest performance.
  • Excluding incomplete ratings was only acceptable in simple scenarios with low missingness under MCAR/MAR.

Conclusions:

  • No single method for handling missing data is universally optimal for AC1 estimation.
  • Excluding zero ratings is recommended for balanced prevalence or MCAR/MAR scenarios.
  • Subject mode imputation is advised for skewed prevalence under MNAR.
  • Researchers should consider reporting AC1 estimates using multiple methods to assess sensitivity.