Accurate sample size calculation is essential for ensuring the reliability and validity of diagnostic test evaluations. Whether you aim to assess the performance of a single diagnostic method (e.g., sensitivity, specificity, AUC) or compare the accuracy of two diagnostic tests, proper sample size estimation helps optimize study design, control costs, and achieve sufficient statistical power.
Estimate the required sample size to validate performance metrics such as sensitivity, specificity, or the area under the ROC curve (AUC).
Determine the sample size needed to detect significant differences in sensitivity, specificity, positive/negative predictive values (PPV/NPV) or AUC between two diagnostic methods (paired or unpaired designs).
For complex study designs (e.g., stratified or clustered sampling) or extreme expected proportions (e.g., sensitivity >95%), customized methods may be required. Our team can assist with advanced statistical support. We also provide developer-friendly python package for sample size calculations, ideal for integration into your research workflows.