Estimation of the diagnostic threshold accounting for decision costs and sampling uncertainty.
Medical diagnostic tests are used to classify subjects as non-diseased or diseased. The classification rule usually consists of classifying subjects using the values of a continuous marker that is dichotomised by means of a threshold. Here, the optimum threshold estimate is found by minimising a cost function that accounts for both decision costs and sampling uncertainty. The cost function is optimised either analytically in a normal distribution setting or empirically in a free-distribution setting when the underlying probability distributions of diseased and non-diseased subjects are unknown. Inference of the threshold estimates is based on approximate analytically standard errors and bootstrap-based approaches. The performance of the proposed methodology is assessed by means of a simulation study, and the sample size required for a given confidence interval precision and sample size ratio is also calculated. Finally, a case example based on previously published data concerning the diagnosis of Alzheimer's patients is provided in order to illustrate the procedure.
Public Health Department, University of Barcelona, Casanova, Spain.
Konstantina
K
Skaltsa
Lluís
L
Jover
Josep Lluís
JL
Carrasco
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis; D001699:Biometry / Q000379:methods*; D003657:Decision Making; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted; D003951:Diagnostic Errors / Q000517:prevention & control; D006801:Humans; D015233:Models, Statistical; D015203:Reproducibility of Results; D018401:Sample Size