Modeling the ratio of correlated biomarkers using copula regression
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| Title: | Modeling the ratio of correlated biomarkers using copula regression |
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| Authors: | Moritz Berger, Nadja Klein, Michael Wagner, Matthias Schmid |
| Source: | Stat Methods Med Res Statistical methods in medical research 34(5), 968-985 (2025). doi:10.1177/09622802241313293 Statistical Methods in Medical Research |
| Publication Status: | Preprint |
| Publisher Information: | SAGE Publications, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | distributional regression, FOS: Computer and information sciences, ddc:000, information & general works, tau Proteins, Methodology (stat.ME), ratio outcome, Original Research Articles, Humans, Computer Simulation, ddc:610, Computer science, information & general works, Statistics - Methodology, Amyloid beta-Peptides, Models, Statistical, gamma distribution, diagnosis [Alzheimer Disease], Computer science, Copula model, Regression Analysis, negative dependence, Biomarkers |
| Description: | Modeling the ratio of two dependent components as a function of covariates is a frequently pursued objective in observational research. Despite the high relevance of this topic in medical studies, where biomarker ratios are often used as surrogate endpoints for specific diseases, existing models are commonly based on oversimplified assumptions, assuming e.g. independence or strictly positive associations between the components. In this paper, we overcome such limitations and propose a regression model where the marginal distributions of the two components are linked by a copula. A key feature of our model is that it allows for both positive and negative associations between the components, with one of the model parameters being directly interpretable in terms of Kendall’s rank correlation coefficient. We study our method theoretically, evaluate finite sample properties in a simulation study and demonstrate its efficacy in an application to diagnosis of Alzheimer’s disease via ratios of amyloid-beta and total tau protein biomarkers. |
| Document Type: | Article Other literature type |
| File Description: | application/pdf |
| Language: | English |
| ISSN: | 1477-0334 0962-2802 |
| DOI: | 10.1177/09622802241313293 |
| DOI: | 10.48550/arxiv.2312.00439 |
| DOI: | 10.5445/ir/1000179001 |
| Access URL: | https://pubmed.ncbi.nlm.nih.gov/39930915 http://arxiv.org/abs/2312.00439 https://pub.dzne.de/record/279354 https://publikationen.bibliothek.kit.edu/1000179001 https://doi.org/10.5445/IR/1000179001 https://publikationen.bibliothek.kit.edu/1000179001/156986603 |
| Rights: | arXiv Non-Exclusive Distribution CC BY URL: https://journals.sagepub.com/page/policies/text-and-data-mining-license URL: http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0/) which permits any 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 (http://us.sagepub.com/en-us/nam/open-access-at-sage). |
| Accession Number: | edsair.doi.dedup.....e669ca711f03e791e284d14e89bd87ee |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | Modeling the ratio of two dependent components as a function of covariates is a frequently pursued objective in observational research. Despite the high relevance of this topic in medical studies, where biomarker ratios are often used as surrogate endpoints for specific diseases, existing models are commonly based on oversimplified assumptions, assuming e.g. independence or strictly positive associations between the components. In this paper, we overcome such limitations and propose a regression model where the marginal distributions of the two components are linked by a copula. A key feature of our model is that it allows for both positive and negative associations between the components, with one of the model parameters being directly interpretable in terms of Kendall’s rank correlation coefficient. We study our method theoretically, evaluate finite sample properties in a simulation study and demonstrate its efficacy in an application to diagnosis of Alzheimer’s disease via ratios of amyloid-beta and total tau protein biomarkers. |
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| ISSN: | 14770334 09622802 |
| DOI: | 10.1177/09622802241313293 |