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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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Sample size re-estimation in paired comparative diagnostic accuracy studies with a binary response.

Gareth P J McCray1, Andrew C Titman2, Paula Ghaneh3

  • 1Institute of Primary Care and Health Sciences, Keele University, David Weatherall Building, Stoke-on-Trent, ST5 5BG, UK. g.mccray@keele.ac.uk.

BMC Medical Research Methodology
|July 15, 2017
PubMed
Summary
This summary is machine-generated.

Accurately estimating sample size for paired diagnostic accuracy studies is crucial. This method uses interim analysis to re-estimate sample size, reducing participant numbers while maintaining study power.

Keywords:
Diagnostic accuracyInterim analysisSample-size re-estimationSensitivitySpecificityStudy design

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Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Clinical Trial Design

Background:

  • Sample size in paired comparative diagnostic accuracy studies depends on test dependence.
  • Accurate sample size determination is challenging due to unknown test dependence.
  • Overpowering studies is wasteful; interim analysis offers a solution.

Purpose of the Study:

  • To present a sample size estimation and re-estimation method for paired diagnostic accuracy studies.
  • To address the challenge of unknown conditional dependence between diagnostic tests.
  • To optimize study resource allocation and participant burden.

Main Methods:

  • Utilizes maximum likelihood estimates under a multinomial model at a planned interim analysis.
  • Estimates conditional dependence between tests and, if needed, prevalence.
  • Illustrated with a study comparing diagnostic procedures for pancreatic cancer detection.

Main Results:

  • The proposed method maintains stable Type I error rates.
  • Type II error rates are at or above nominal levels.
  • Situations requiring lower sample sizes have Type II error rates above nominal, minimizing participant impact.

Conclusions:

  • Recommends multinomial model maximum likelihood estimation at interim analysis.
  • This approach effectively reduces the required number of participants.
  • Ensures studies are adequately powered to the nominal level.