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Estimating transformations for evaluating diagnostic tests with covariate adjustment

Receiver operating characteristic analysis is one of the most popular approaches for evaluating and comparing the accuracy of medical diagnostic tests. Although various methodologies have been developed for estimating receiver operating characteristic curves and their associated summary indices, the...

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Autores principales: Sewak, Ainesh, Hothorn, Torsten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500951/
https://www.ncbi.nlm.nih.gov/pubmed/37278185
http://dx.doi.org/10.1177/09622802231176030
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author Sewak, Ainesh
Hothorn, Torsten
author_facet Sewak, Ainesh
Hothorn, Torsten
author_sort Sewak, Ainesh
collection PubMed
description Receiver operating characteristic analysis is one of the most popular approaches for evaluating and comparing the accuracy of medical diagnostic tests. Although various methodologies have been developed for estimating receiver operating characteristic curves and their associated summary indices, there is no consensus on a single framework that can provide consistent statistical inference while handling the complexities associated with medical data. Such complexities might include non-normal data, covariates that influence the diagnostic potential of a test, ordinal biomarkers or censored data due to instrument detection limits. We propose a regression model for the transformed test results which exploits the invariance of receiver operating characteristic curves to monotonic transformations and accommodates these features. Simulation studies show that the estimates based on transformation models are unbiased and yield coverage at nominal levels. The methodology is applied to a cross-sectional study of metabolic syndrome where we investigate the covariate-specific performance of weight-to-height ratio as a non-invasive diagnostic test. Software implementations for all the methods described in the article are provided in the tram add-on package to the R system for statistical computing and graphics.
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spelling pubmed-105009512023-09-15 Estimating transformations for evaluating diagnostic tests with covariate adjustment Sewak, Ainesh Hothorn, Torsten Stat Methods Med Res Original Research Articles Receiver operating characteristic analysis is one of the most popular approaches for evaluating and comparing the accuracy of medical diagnostic tests. Although various methodologies have been developed for estimating receiver operating characteristic curves and their associated summary indices, there is no consensus on a single framework that can provide consistent statistical inference while handling the complexities associated with medical data. Such complexities might include non-normal data, covariates that influence the diagnostic potential of a test, ordinal biomarkers or censored data due to instrument detection limits. We propose a regression model for the transformed test results which exploits the invariance of receiver operating characteristic curves to monotonic transformations and accommodates these features. Simulation studies show that the estimates based on transformation models are unbiased and yield coverage at nominal levels. The methodology is applied to a cross-sectional study of metabolic syndrome where we investigate the covariate-specific performance of weight-to-height ratio as a non-invasive diagnostic test. Software implementations for all the methods described in the article are provided in the tram add-on package to the R system for statistical computing and graphics. SAGE Publications 2023-06-06 2023-07 /pmc/articles/PMC10500951/ /pubmed/37278185 http://dx.doi.org/10.1177/09622802231176030 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research Articles
Sewak, Ainesh
Hothorn, Torsten
Estimating transformations for evaluating diagnostic tests with covariate adjustment
title Estimating transformations for evaluating diagnostic tests with covariate adjustment
title_full Estimating transformations for evaluating diagnostic tests with covariate adjustment
title_fullStr Estimating transformations for evaluating diagnostic tests with covariate adjustment
title_full_unstemmed Estimating transformations for evaluating diagnostic tests with covariate adjustment
title_short Estimating transformations for evaluating diagnostic tests with covariate adjustment
title_sort estimating transformations for evaluating diagnostic tests with covariate adjustment
topic Original Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500951/
https://www.ncbi.nlm.nih.gov/pubmed/37278185
http://dx.doi.org/10.1177/09622802231176030
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