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Comparison of methods to handle missing values in a binary index test in a diagnostic accuracy study - a simulation

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Summary

Handling missing values in diagnostic studies is crucial. Multiple Imputation by Chained Equations (MICE) is recommended for missing at random (MAR) data, outperforming other methods, especially with increasing missingness.

Keywords:
Diagnostic studyMissing valuesSensitivitySimulation studySpecificity

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Area of Science:

  • Biostatistics
  • Diagnostic Test Evaluation
  • Missing Data Analysis

Background:

  • No established guidelines exist for handling missing values in dichotomous index tests within diagnostic studies.
  • Researchers often omit missing data or employ simplistic techniques, potentially biasing results.
  • This study addresses the performance of various methods for estimating sensitivity and specificity with missing index test data.

Purpose of the Study:

  • To compare the performance of selected methods for estimating sensitivity and specificity of a dichotomous index test when missing values are present.
  • To evaluate how different missing data proportions and mechanisms affect the accuracy of these estimation methods.
  • To provide evidence-based recommendations for handling missing data in diagnostic accuracy studies.

Main Methods:

  • Simulation of diagnostic study data with a dichotomous reference standard, index test, and covariates.
  • Modeling of missing values in the index test under various proportions and missingness mechanisms (MCAR, MAR, MNAR).
  • Comparison of seven methods: complete case analysis, worst case scenario (WC), random hot deck, Multiple Imputation by Chained Equations (MICE), and three product multinomial approaches.

Main Results:

  • Most methods are unbiased under Missing Completely At Random (MCAR), except for WC.
  • Under Missing At Random (MAR), MICE demonstrates superior performance with minimal bias and best coverage probability.
  • All tested methods exhibit substantial bias and inadequate coverage under Missing Not At Random (MNAR) conditions.

Conclusions:

  • MICE is the preferred method for handling missing values in diagnostic studies when data are MAR, particularly as the proportion of missingness increases.
  • Simple methods and complete case analysis may suffice for small proportions of missing data under MCAR.
  • No tested method is suitable for situations where missing values are not at random (MNAR).