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Estimation of the diagnostic threshold accounting for decision costs and sampling uncertainty
Author(s) -
Skaltsa Konstantina,
Jover Lluís,
Carrasco Josep Lluís
Publication year - 2010
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200900294
Subject(s) - statistics , sampling (signal processing) , sample size determination , confidence interval , mathematics , inference , sample (material) , econometrics , sampling distribution , computer science , artificial intelligence , chemistry , filter (signal processing) , chromatography , computer vision
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.