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Journal of Investigative Medicine:
doi: 10.231/JIM.0000000000000066
Research Tools and Issues

Improvement of Sample Size Calculations for Binary Diagnostic Test Assessment

Bailly, Sébastien MSc*†‡§; Dupont, Cyrielle MSc*†‡§; Iwaz, Jean PhD*†‡§; Bossard, Nadine MD, PhD*†‡§; Rabilloud, Muriel MD, PhD*†‡§

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Abstract

Objective

This study aimed to formulate a new R function to improve sample size calculation for more accurate estimations of sensitivity (Se) and specificity (Sp).

Methods

The developed function is based on the binDesign function of the binGroup R package. This allowed the use of an “exact” method based on the binomial distribution. In addition, the function takes into account a joint testing of Se and Sp and a nonmonotonous behavior of the power function.

Results

Four tables were generated to display the number of cases (or controls) in joint or separate assessments for an expected combination of Se (or Sp) and a determined difference between the expected Se (or Sp) and the minimum acceptable Se (or Sp). Using the formula for a joint testing of Se and Sp, it resulted in a higher increase of the sample sizes than simply allowing for the sawtooth shape of the power curve.

Conclusion

Whenever equal Se and Sp values are important, a joint testing should be favored and used for sample size determination.

Copyright © 2014 by The American Federation for Medical Research

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