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Improvement of Sample Size Calculations for Binary Diagnostic Test Assessment
  1. Sébastien Bailly, MSc*†‡§,
  2. Cyrielle Dupont, MSc*†‡§,
  3. Jean Iwaz, PhD*†‡§,
  4. Nadine Bossard, MD, PhD*†‡§,
  5. Muriel Rabilloud, MD, PhD*†‡§
  1. From 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.
  1. Received November 22, 2013, and in revised form January 14, 2014.
  2. Accepted for publication January 14, 2014.
  3. Reprints: Sébastien Bailly, PhD, Service de Biostatistique, Hospices Civils de Lyon, 162, avenue Lacassagne, F-69003, Lyon, France. E-mail: sbailly{at}


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.

Key Words
  • diagnostic tests
  • sample sizes
  • binomial distribution
  • sensitivity and specificity

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