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Updated: Dec 18, 2025

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation
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Using multiple agreement methods for continuous repeated measures data: a tutorial for practitioners.

Richard A Parker1, Charles Scott2, Vanda Inácio3

  • 1Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK.

BMC Medical Research Methodology
|June 14, 2020
PubMed
Summary
This summary is machine-generated.

This study compares five methods for assessing measurement agreement in Chronic Obstructive Pulmonary Disease (COPD) patients. While methods yield similar conclusions, coverage probability with graphical data aids interpretation and identifies disagreement causes.

Keywords:
AgreementConcordance correlation coefficientLimits of agreementMethod comparison studiesRepeated measures

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Area of Science:

  • Biostatistics
  • Medical Device Evaluation
  • Respiratory Medicine

Background:

  • Agreement studies assess consistency between multiple measurements of the same quantity.
  • Linear mixed modeling offers methods for time-matched repeated measurements within subjects.
  • Choosing appropriate agreement assessment methods is crucial for reliable data interpretation.

Purpose of the Study:

  • To guide practitioners in selecting agreement assessment methods based on linear mixed models.
  • To compare five distinct agreement assessment methods using real-world data.
  • To highlight the strengths and weaknesses of different agreement metrics.

Main Methods:

  • Tutorial on choosing agreement methods under linear mixed model assumptions.
  • Head-to-head comparison of five methods: concordance correlation coefficient, limits of agreement, total deviation index, coverage probability, and coefficient of individual agreement.
  • Application to respiratory rate data from Chronic Obstructive Pulmonary Disease (COPD) patients.

Main Results:

  • All five methods provided comparable overall conclusions on device agreement in the COPD study.
  • Specific methods highlighted different aspects of between-device comparisons.
  • Clarity of interpretation varied among the assessed agreement methods.

Conclusions:

  • Understanding the nuances of each agreement method is vital for researchers.
  • Coverage probability, combined with graphical data displays, is recommended for method comparison studies.
  • Investigating underlying causes of disagreement, beyond summary indices, is essential, aided by graphical summaries and model parameters.