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Improvement of sample size calculations for binary diagnostic test assessment
Sébastien Bailly1, Cyrielle Dupont, Jean Iwaz
1From the *Service de Biostatistique, Hospices Civils de Lyon; †Université de Lyon, Lyon; ‡Université Lyon 1; and §Centre National de la Recherche Scientifique Unité Mixte de Recherche Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique-Santé, Villeurbanne, France.
Summary
A new R function enhances sample size calculations for sensitivity (Se) and specificity (Sp) estimations. Joint testing for Se and Sp is recommended for accurate sample size determination when equal values are crucial.
Area of Science:
- Biostatistics
- Statistical Software Development
Background:
- Accurate sample size calculation is crucial for diagnostic test evaluations.
- Existing methods may not adequately address joint estimations of sensitivity and specificity.
Purpose of the Study:
- To develop a novel R function for improved sample size calculations.
- To enhance the accuracy of sensitivity (Se) and specificity (Sp) estimations.
Main Methods:
- The new R function is based on the 'binDesign' function from the 'binGroup' R package.
- Employs an exact method utilizing the binomial distribution.
- Incorporates joint testing of Se and Sp and accounts for non-monotonous power function behavior.
Main Results:
- Generated four tables illustrating sample size requirements for joint or separate Se/Sp assessments.
- Joint testing of Se and Sp resulted in a greater sample size increase compared to accounting for power curve variations alone.
Conclusions:
- Joint testing is recommended for sample size determination when equal sensitivity and specificity values are important.
- The developed R function provides a more robust approach to sample size calculation for diagnostic accuracy studies.
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