Comparison of mean results for the Uncertain Interval and TG-ROC’s Intermediate Range over 1000 simulations.

<p>Comparison of mean results for the Uncertain Interval and TG-ROC’s Intermediate Range over 1000 simulations.</p

Mixed Probability Histogram for the prediction of capsular penetration in cases of prostate cancer.

<p>Mixed Probability Histogram for the prediction of capsular penetration in cases of prostate cancer.</p

Summary of the probability distributions of patients with and without capsular penetration.

<p>Summary of the probability distributions of patients with and without capsular penetration.</p

Interval of Uncertainty: An Alternative Approach for the Determination of Decision Thresholds, with an Illustrative Application for the Prediction of Prostate Cancer

<div><p>Often, for medical decisions based on test scores, a single decision threshold is determined and the test results are dichotomized into positive and negative diagnoses. It is therefore important to identify the decision threshold with the least number of misclassifications. The proposed method uses trichotomization: it defines an Uncertain Interval around the point of intersection between the two distributions of individuals with and without the targeted disease. In this Uncertain Interval the diagnoses are intermixed and the numbers of correct and incorrect diagnoses are (almost) equal. This Uncertain Interval is considered to be a range of test scores that is inconclusive and does not warrant a decision. It is expected that defining such an interval with some precision, prevents a relatively large number of false decisions, and therefore results in an increased accuracy or correct classifications rate (CCR) for the test scores outside this Uncertain Interval. Clinical data and simulation results confirm this. The results show that the CCR is systematically higher outside the Uncertain Interval when compared to the CCR of the decision threshold based on the maximized Youden index. For strong tests with a very small overlap between the two distributions, it can be difficult to determine an Uncertain Interval. In simulations, the comparison with an existing method for test-score trichotomization, the Two-graph Receiver Operating Characteristic (TG-ROC), showed smaller differences between the two distributions for the Uncertain Interval than for TG-ROC’s Intermediate Range and consequently a more improved CCR outside the Uncertain Interval. The main conclusion is that the Uncertain Interval method offers two advantages: 1. Identification of patients for whom the test results are inconclusive; 2. A higher estimated rate of correct decisions for the remaining patients.</p></div

Density of ‘healthy’ (0) and ‘diseased’ population (1) and the definition of an Uncertain Interval with balanced TN and FP and balanced FN and TP.

<p>Density of ‘healthy’ (0) and ‘diseased’ population (1) and the definition of an Uncertain Interval with balanced TN and FP and balanced FN and TP.</p

Comparison of mean results for Youden, More Certain Interval and TG-ROC’s Valid Range over 1000 simulations.

<p>Comparison of mean results for Youden, More Certain Interval and TG-ROC’s Valid Range over 1000 simulations.</p

Observed results within the Uncertain Interval.

<p>Observed results within the Uncertain Interval.</p

Results of various methods for decision threshold determination applied to the probabilities that are predictive of capsular penetration.

<p>Results of various methods for decision threshold determination applied to the probabilities that are predictive of capsular penetration.</p

TG-ROC graphic.

<p>TG-ROC graphic.</p

Mixed Probability Histogram for the prediction of capsular penetration for patients within the Uncertain Interval.

<p>Mixed Probability Histogram for the prediction of capsular penetration for patients within the Uncertain Interval.</p