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.


CONCLUSIONS
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.
Klotsche
Jens
J
Institute of Clinical Psychology and Psychotherapy, Technische Universitaet Dresden, Germany. klotsche@psychologie.tu-dresden.de
Ferger
Dietmar
D
Pieper
Lars
L
Rehm
Jürgen
J
Wittchen
Hans-Ulrich
HU
D000293:Adolescent; D000328:Adult; D000368:Aged; D001837:Body Weights and Measures; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D012372:ROC Curve; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods / Q000706:statistics & numerical data*; D018709:Statistics, Nonparametric; D055815:Young Adult