Accurate diagnostic test performance assessment is critical in clinical and research settings, where
a wide range of metrics, including accuracy, sensitivity (recall), specificity, f1 score, positive
predictive value (PPV), and negative predictive value (NPV), are used to evaluate test validity.
However, heterogeneous reporting of these metrics across studies poses challenges for direct
comparison and meta-analysis. RetroCalc, a novel program, addresses this gap by reconstructing
essential confusion matrix values—true positives (TP), true negatives (TN), false positives (FP),
and false negatives (FN)—from minimal input pairs. RetroCalc enables accurate estimation of all
relevant diagnostic metrics, even when only limited data is provided. This feature supports meta-analyses by harmonizing data inputs, facilitating more consistent and comprehensive reviews of
diagnostic test accuracy studies. RetroCalc provides a valuable tool for enhancing the
interpretability and comparability of diagnostic test outcomes, supporting a more unified and
accessible approach to diagnostic accuracy research.