Improvement of Sample Size Calculations for Binary Diagnostic Test Assessment. (1st April 2014)

• Record Type: Journal Article
• Authors: Bailly, Sébastien; Dupont, Cyrielle; Iwaz, Jean; Bossard, Nadine; Rabilloud, Muriel
• Is Part Of: Journal of investigative medicine. Volume 62:Number 4(2014)
• Page Start: 687
• Page End: 689
• Publication Date: 2014-04-01
• Subjects: diagnostic tests -- sample sizes -- binomial distribution -- sensitivity and specificity; Clinical medicine -- Periodicals; Medicine -- Research -- Periodicals; Medicine; Research -- United States; Clinical medicine; Medicine -- Research; Periodicals; 616.075
• DOI: 10.2310/JIM.0000000000000066
• Languages: English
• ISSNs: 1081-5589

Abstract

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.