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.