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Related Concept Videos

Arithmetic Mean01:08

Arithmetic Mean

The arithmetic mean is the most commonly used measure of the central tendency of a data set. It is defined as the sum of all the elements constituting the data set, divided by the total number of elements. It is sometimes loosely referred to as the “average.”
When all the values in a data set are not unique, the sum in the numerator can be calculated by multiplying each distinct value by its frequency.
Sometimes, the arithmetic mean of a sample can be affected by a few data points that are...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Multiple Bar Graph01:07

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects or...
Geometric Mean01:15

Geometric Mean

The mean is a measure of the central tendency of a data set. In some data sets, the data is inherently multiplicative, and the arithmetic mean is not useful. For example, the human population multiplies with time, and so does the credit amount of financial investment, as the interest compounds over successive time intervals.
In cases of multiplicative data, the geometric mean is used for statistical analysis. First, the product of all the elements is taken. Then, if there are n elements in the...
Central Tendency: Analysis01:10

Central Tendency: Analysis

Measures of central tendency are tools used in biostatistics to identify the average or center of a dataset. They offer a single representative value for understanding and summarizing data distribution.
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
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Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness
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Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness

Published on: March 18, 2022

A graphical method for assessing agreement with the mean between multiple observers using continuous measures.

Mark Jones1, Annette Dobson, Sue O'Brian

  • 1The University of Queensland, School of Population Health, Queensland, Australia. m.jones@sph.uq.edu.au

International Journal of Epidemiology
|July 9, 2011
PubMed
Summary
This summary is machine-generated.

A new graphical method visually assesses agreement among multiple observers for continuous measurements. This method extends the Bland-Altman technique, offering clinically relevant insights into measurement reproducibility beyond traditional measures like intra-class correlation (ICC).

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Area of Science:

  • Biostatistics
  • Medical Imaging Analysis
  • Reproducibility Studies

Background:

  • Lack of simple graphical methods to assess agreement among multiple observers for continuous measurements.
  • Existing methods often fail to provide a clear visual representation of multi-observer agreement.

Purpose of the Study:

  • To develop and illustrate a straightforward graphical method for evaluating agreement between multiple observers using continuous data.
  • To extend the established Bland-Altman graphical method to accommodate more than two observers.

Main Methods:

  • Modification and extension of the Bland-Altman graphical method for multiple observers.
  • Derivation of mathematical formulas to support the graphical assessment.
  • Illustration using real-world data examples, including measurements of lung lesions.

Main Results:

  • The proposed method provides clinically useful information on limits of agreement with the mean.
  • Visual assessment of limits of agreement across the range of measurements is enabled.
  • Example with five readers showed high intra-class correlation (ICC=0.84) but clinically significant limits of agreement (-1.1 to 1.1 cm), indicating potential measurement discrepancies.
  • Analysis revealed heterogeneous agreement, possibly linked to lesion edge definition variability.

Conclusions:

  • The developed graphical method is a valuable tool for assessing multi-observer agreement in continuous measurements.
  • It complements existing statistical measures like intra-class correlation (ICC) for reporting reproducibility.
  • The method aids in identifying clinically significant variations in measurements among observers.