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Classification accuracy goals for diagnostic tests based on risk stratification

Classification accuracy goals for diagnostic tests based on risk stratification In diagnostic test evaluation, performance goals are often set for classification accuracy measures such as specificity, sensitivity and diagnostic likelihood ratio. For tests that detect rare conditions, classification accuracy goals are attractive because they can be evaluated in case-control studies enriched for the condition. A neglected area of research is determining classification accuracy goals that confer clinical usefulness of a test. We determine classification accuracy goals based on desired risk stratification, i.e. the post-test risk of having the condition compared with the pre-test risk. We determine goals for rule-out tests, rule-in tests, and those that do both. Goals for negative and positive likelihood ratios (NLR, PLR) are emphasized because of their natural relationships with risk stratification via Bayes Theorem. Goals for specificity and sensitivity are implied by goals on NLR and PLR. Goals that confer superiority or non-inferiority of a test to a comparator are based on approximating risk differences and relative risks by functions of likelihood ratios. Inference is based on Wald confidence intervals for ratios of likelihood ratios. To illustrate, we consider hypothetical data on a fetal fibronectin assay for ruling out risk of pre-term birth and two human papillomavirus assays for detecting cervical cancer. Trial registration ClinicalTrials.gov identifier: NCT01931566. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Classification accuracy goals for diagnostic tests based on risk stratification

Biostatistics & Epidemiology , Volume 5 (2): 20 – Jul 3, 2021

Classification accuracy goals for diagnostic tests based on risk stratification

Abstract

In diagnostic test evaluation, performance goals are often set for classification accuracy measures such as specificity, sensitivity and diagnostic likelihood ratio. For tests that detect rare conditions, classification accuracy goals are attractive because they can be evaluated in case-control studies enriched for the condition. A neglected area of research is determining classification accuracy goals that confer clinical usefulness of a test. We determine classification accuracy goals...
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Publisher
Taylor & Francis
Copyright
This work was authored as part of the Contributor's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 USC. 105, no copyright protection is available for such works under US Law.
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2021.1878406
Publisher site
See Article on Publisher Site

Abstract

In diagnostic test evaluation, performance goals are often set for classification accuracy measures such as specificity, sensitivity and diagnostic likelihood ratio. For tests that detect rare conditions, classification accuracy goals are attractive because they can be evaluated in case-control studies enriched for the condition. A neglected area of research is determining classification accuracy goals that confer clinical usefulness of a test. We determine classification accuracy goals based on desired risk stratification, i.e. the post-test risk of having the condition compared with the pre-test risk. We determine goals for rule-out tests, rule-in tests, and those that do both. Goals for negative and positive likelihood ratios (NLR, PLR) are emphasized because of their natural relationships with risk stratification via Bayes Theorem. Goals for specificity and sensitivity are implied by goals on NLR and PLR. Goals that confer superiority or non-inferiority of a test to a comparator are based on approximating risk differences and relative risks by functions of likelihood ratios. Inference is based on Wald confidence intervals for ratios of likelihood ratios. To illustrate, we consider hypothetical data on a fetal fibronectin assay for ruling out risk of pre-term birth and two human papillomavirus assays for detecting cervical cancer. Trial registration ClinicalTrials.gov identifier: NCT01931566.

Journal

Biostatistics & EpidemiologyTaylor & Francis

Published: Jul 3, 2021

Keywords: Bayes theorem; rare disease assumption; likelihood ratio graph; discrimination; medical decision making

References