title: Improvement of sample size calculations for binary diagnostic test assessment.
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.
  
  
  CONCLUSIONS
  Whenever equal Se and Sp values are important, a joint testing should be favored and used for sample size determination.
authors.affiliation: 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.
authors.forename: Sébastien
authors.initials: S
authors.lastname: Bailly
authors.forename: Cyrielle
authors.initials: C
authors.lastname: Dupont
authors.forename: Jean
authors.initials: J
authors.lastname: Iwaz
authors.forename: Nadine
authors.initials: N
authors.lastname: Bossard
authors.forename: Muriel
authors.initials: M
authors.lastname: Rabilloud
mesh_terms: D016022:Case-Control Studies; D003955:Diagnostic Tests, Routine; D006801:Humans; D018401:Sample Size; D013223:Statistics as Topic