There are several criteria for determination of the most appropriate cut-off value in a diagnostic test with continuous results. Mostly based on receiver operating characteristic (ROC) analysis, there are various methods to determine the test cut-off value. The most common criteria are the point on ROC curve where the sensitivity and specificity of the test are equal; the point on the curve wit...

BACKGROUND Adherence to tuberculosis (TB) treatment is troublesome, due to long therapy duration, quick therapeutic response which allows the patient to disregard about the rest of their treatment and the lack of motivation on behalf of the patient for improved. The objective of this study was to develop and validate a scoring system to predict the probability of lost to follow-up outcome in TB...

OBJECTIVE To propose a cut-off for the World Health Organization Quality of Life-Bref (WHOQOL-bref) as a predictor of quality of life in older adults.METHODS Cross-sectional study with 391 older adults registered in the Northwest Health District in Belo Horizonte, MG, Southeastern Brazil, between October 8, 2010 and May 23, 2011. The older adults' quality of life was measured using the WHOQOL-b...

1 AUC Estimation The area under the ROC curve can be approximated using lower rectangles, upper rectangles or by using a linear approximation, as shown in Figure 1. The expressions related to each of these approximations are Al(zA) = M−1 ∑ t=0 (1− M̂Rt)(F̂ARt+1 − F̂ARt) Au(zA) = M−1 ∑ t=0 (1− M̂Rt+1)(F̂ARt+1 − F̂ARt) Am(zA) = M−1 ∑ t=0 (1− M̂Rt + M̂Rt+1 2 )(F̂ARt+1 − F̂ARt). Substituting the estimates fo...

Abstract One of the most common metrics to evaluate neural network classifiers is area under receiver operating characteristic curve (AUC). However, optimisation AUC as loss function during training not a standard procedure. Here we compare minimising cross-entropy (CE) and optimising directly. In particular, analyse landscape (LFL) approximate (appAUC) functions discover organisation this solut...

Receiver operating characteristic (ROC) curves evaluate the discriminatory power of a continuous marker to predict a binary outcome. The most popular parametric model for an ROC curve is the binormal model, which assumes that the marker, after a monotone transformation, is normally distributed conditional on the outcome. Here, the authors present an alternative to the binormal model based on th...

BACKGROUND This study was performed to compare the efficacies of Injury Severity Score (ISS) and New Injury Severity Score (NISS), and to investigate whether replacing ISS with NISS in Trauma and Injury Severity Score (TRISS) changes the predictive power for mortality. METHODS We retrospectively analyzed 550 patients aged over 16 years seen in our center over a period of four years who had in...

BACKGROUND Using photographs of occlusal surfaces instead of extracted teeth for the detection of caries can be useful in multicenter studies or education. Using a panel of observers, ICDAS scores on teeth or photographs were evaluated against the histological gold standard. The hypothesis was that both outcomes were equivalent. METHODS Four examiners with different experience in ICDAS scored...

Covariate-specific time-dependent ROC curves are often used to evaluate the diagnostic accuracy of a biomarker with time-to-event outcomes, when certain covariates have an impact on the test accuracy. In many medical studies, measurements of biomarkers are subject to missingness due to high cost or limitation of technology. This article considers estimation of covariate-specific time-dependent ...

INTRODUCTION We examined the design, analysis and reporting in multi-reader multi-case (MRMC) research studies using the area under the receiver-operating curve (ROC AUC) as a measure of diagnostic performance. METHODS We performed a systematic literature review from 2005 to 2013 inclusive to identify a minimum 50 studies. Articles of diagnostic test accuracy in humans were identified via the...