Binary regression: total gain in positive and negative predictive values.

Models that predict disease incidence or disease recurrence are attractive for clinicians as well as for patients. The usefulness of a risk prediction model is linked to the two questions whether the observed outcome is confirmed by the prediction and whether the risk prediction is accurate in predicting the future outcome, respectively. The first phrasing of the question is linked to considering sensitivity and specificity and the latter to the positive and negative predictive values. We present the measures of standardized total gain in positive and negative predictive values dealing with the performance or accuracy of the prediction model for a binary outcome. Both measures provide a useful tool for assessing the performance or accuracy of a set of predictor variables for the prediction of a binary outcome. This concept is a tool for evaluating the optimal prediction model in future research.

A novel nonparametric approach for estimating cut-offs in continuous risk indicators with application to diabetes epidemiology.

BACKGROUND: Epidemiological and clinical studies, often including anthropometric measures, have established obesity as a major risk factor for the development of type 2 diabetes. Appropriate cut-off values for anthropometric parameters are necessary for prediction or decision purposes. The cut-off corresponding to the Youden-Index is often applied in epidemiology and biomedical literature for dichotomizing a continuous risk indicator. METHODS: Using data from a representative large multistage longitudinal epidemiological study in a primary care setting in Germany, this paper explores a novel approach for estimating optimal cut-offs of anthropomorphic parameters for predicting type 2 diabetes based on a discontinuity of a regression function in a nonparametric regression framework. RESULTS: The resulting cut-off corresponded to values obtained by the Youden Index (maximum of the sum of sensitivity and specificity, minus one), often considered the optimal cut-off in epidemiological and biomedical research. The nonparametric regression based estimator was compared to results obtained by the established methods of the Receiver Operating Characteristic plot in various simulation scenarios and based on bias and root mean square error, yielded excellent finite sample properties. CONCLUSION: It is thus recommended that this nonparametric regression approach be considered as valuable alternative when a continuous indicator has to be dichotomized at the Youden Index for prediction or decision purposes.

Pupil diameter in darkness differentiates progressive supranuclear palsy (PSP) from other extrapyramidal syndromes.

The most important features that characterize and differentiate progressive supranuclear palsy from other Parkinsonian syndromes are postural instability, supranuclear gaze palsy, pseudobulbar palsy, parkinsonism, and cognitive disturbances. In this article, we demonstrate that progressive supranuclear palsy patients exhibit pathologically decreased pupil diameters after dark adaptation recorded by TV pupillography. A cut off value of 3.99 mm was defined to differentiate progressive supranuclear palsy patients from patients with other extrapyramidal disorders like Parkinson's disease and multiple system atrophy with a specificity of 86.4% and a sensitivity of 70.8%. Other pupil abnormalities could not be described in patients with extrapyramidal syndromes.