ePositive predictive value, proportion of patients predicted to have SWG who had substantial weight gain (estimated using leave-one-out cross validation).
fNegative predictive value, proportion of patients predicted not to have SWG who did not have substantial weight gain (estimated using leave-one-out cross validation).
Studies ascertaining cases of type 2 diabetes by self report involved uncertainty in ascertainment, and thus numbers of cases were revised by a positive predictive value (proportion of verified cases among self reported cases) (see supplementary information and table S3).
Analogous Figures 1 and 2 (the boxplots showing the predicted WC values) and Tables 5 and 6 (the validation exercise assessing the sensitivity, specificity, positive predictive value, and proportion correctly predicted) are available upon request.
Accuracies, sensitivity, specificity, positive predictive values (the proportion of patients among those predicted to relapse who actually relapsed) and negative predictive values (the proportion of patients among those predicted to be non-relapsers who did not relapse within 3 years) were validated.
When the optimal probability cut-off was used to predict cycle 1 FN, test characteristics were: sensitivity 81%; specificity 80%; positive predictive value 28% (proportion of patients classified as high risk who suffered cycle 1 FN); negative predictive value 98% (the proportion of patients classified as low FN risk who did not suffer cycle 1 FN).
The models were compared according to the percentage of patients who were correctly classified, the positive predictive value (PPV; proportion of nonprogressing patients correctly classified as nonprogressing), and the negative predictive value (NPV; proportion of progressing patients correctly classified as progressing).
For each optimal cut-off value we computed sensitivity (true-positive rate), specificity (true-negative rate), positive predictive value (PPV; proportion of patients with positive test results who were correctly diagnosed), negative predictive value (NPV; proportion of patients with negative test results who were correctly diagnosed) and accuracy.
7. Available data suggest that in searching for predictive biomarkers, the proportion of patients who will be biomarker-positive will generally be approximately twice the size of the proportion responding or approximately 5-10% less than the proportion of patients achieving >10% reduction in tumor diameter.
