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Assessing assay agreement estimation for multiple left-censored data: a multiple imputation approach.

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  • 1SBIM, Hôpital Saint-Louis, APHP, Paris, France; Université Paris Diderot, Paris 7, SPC, Paris, France; INSERM, UMR_S 1136, Institut Pierre Louis d'Épidémiologie et de Santé Publique, F-75013, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136, Institut Pierre Louis d'Épidémiologie et de Santé Publique, F-75013, Paris, France.

Statistics in Medicine
|October 9, 2014
PubMed
Summary

This study introduces an improved method for measuring agreement between diagnostic assays, particularly when data is limited. The proposed multiple imputation by chained equations (MICE) approach enhances accuracy for concordance correlation coefficient (CCC) estimation in left-censored data.

Keywords:
agreementcensoringmissing datamultiple imputation: chained equations

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Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Assay Development

Background:

  • Assay agreement is crucial for reliable diagnostic results.
  • Concordance correlation coefficient (CCC) is commonly used but sensitive to left-censored data.
  • Lower limits of detection in assays frequently result in left-censoring.

Purpose of the Study:

  • To propose and evaluate an extended multiple imputation approach by chained equations (MICE) for estimating CCC with left-censored assay data.
  • To compare the performance of the proposed MICE method against existing maximum likelihood estimation techniques.
  • To address the challenge of left-censoring in assay quantification data.

Main Methods:

  • Extension of the multiple imputation by chained equations (MICE) method to handle left-censored data from two assays.
  • Comparison of the proposed two-step MICE approach with a previously published maximum likelihood estimation method.
  • Performance evaluation through a comprehensive simulation study.

Main Results:

  • Both the MICE approach and maximum likelihood estimation provided close estimates of the CCC.
  • The proposed MICE method demonstrated improved coverage compared to maximum likelihood estimation.
  • The methods were applied to real-world cytomegalovirus quantification data.

Conclusions:

  • The extended MICE approach is a robust method for estimating CCC in the presence of left-censored assay data.
  • The MICE method offers improved statistical coverage, leading to more reliable agreement estimates.
  • This approach enhances the accuracy of assay comparison, particularly in biological and medical research settings.