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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
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A sequential test for assessing observed agreement between raters.

Sotiris Bersimis1, Athanasios Sachlas1, Subha Chakraborti2

  • 1Department of Statistics and Insurance Science, University of Piraeus, 18534, Piraeus, Greece.

Biometrical Journal. Biometrische Zeitschrift
|September 13, 2017
PubMed
Summary

A new nonparametric sequential test rapidly assesses rater agreement without needing large sample sizes or contingency tables. This method offers a faster, more efficient tool for medical practitioners evaluating agreement between multiple raters and characteristics.

Keywords:
Cohen's kMarkov chain embedding techniqueagreement assessmenthypothesis testingsequential testing

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Area of Science:

  • Medical Statistics
  • Biostatistics
  • Health Services Research

Background:

  • Assessing inter-rater reliability is crucial in medical practice.
  • Current methods using contingency tables require substantial sample sizes, causing delays.
  • A need exists for rapid agreement assessment as data becomes available.

Purpose of the Study:

  • Introduce a nonparametric sequential test for assessing rater agreement.
  • Develop a method that avoids contingency tables and facilitates rapid assessment.
  • Unify the assessment of agreement for multiple raters and characteristics.

Main Methods:

  • A nonparametric sequential test based on cumulative disagreements.
  • Utilizes waiting time until a cumulative sum exceeds a threshold.
  • Applies to various scenarios including multiple raters, characteristics, and categories.

Main Results:

  • The proposed test demonstrates excellent performance in numerical investigations.
  • Requires significantly smaller sample sizes than existing methods for equivalent statistical power.
  • The method is easily generalizable and performs well even when raters strongly disagree.

Conclusions:

  • The novel sequential test provides a rapid, efficient, and unified framework for assessing rater agreement.
  • Offers a practical alternative to traditional methods, requiring less data and time.
  • Empowers medical practitioners with an easy-to-use tool for reliable agreement assessment.