Estimation of the diagnostic threshold accounting for decision costs and sampling uncertainty |
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Authors: | Skaltsa Konstantina Jover Lluís Carrasco Josep Lluís |
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Institution: | Public Health Department, University of Barcelona, Casanova, Spain. |
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Abstract: | 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. |
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Keywords: | Cost function Cut‐off Diagnostic tests ROC curves Threshold |
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