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000279354 041__ $$aEnglish
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000279354 1001_ $$00000-0002-0656-5286$$aBerger, Moritz$$b0
000279354 245__ $$aModeling the ratio of correlated biomarkers using copula regression.
000279354 260__ $$aLondon [u.a.]$$bSage$$c2025
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000279354 520__ $$aModeling the ratio of two dependent components as a function of covariates is a frequently pursued objective in observational research. Despite the high relevance of this topic in medical studies, where biomarker ratios are often used as surrogate endpoints for specific diseases, existing models are commonly based on oversimplified assumptions, assuming e.g. independence or strictly positive associations between the components. In this paper, we overcome such limitations and propose a regression model where the marginal distributions of the two components are linked by a copula. A key feature of our model is that it allows for both positive and negative associations between the components, with one of the model parameters being directly interpretable in terms of Kendall's rank correlation coefficient. We study our method theoretically, evaluate finite sample properties in a simulation study and demonstrate its efficacy in an application to diagnosis of Alzheimer's disease via ratios of amyloid-beta and total tau protein biomarkers.
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000279354 650_7 $$2Other$$aCopula model
000279354 650_7 $$2Other$$adistributional regression
000279354 650_7 $$2Other$$agamma distribution
000279354 650_7 $$2Other$$anegative dependence
000279354 650_7 $$2Other$$aratio outcome
000279354 650_7 $$2NLM Chemicals$$aBiomarkers
000279354 650_7 $$2NLM Chemicals$$aAmyloid beta-Peptides
000279354 650_7 $$2NLM Chemicals$$atau Proteins
000279354 650_2 $$2MeSH$$aBiomarkers
000279354 650_2 $$2MeSH$$aHumans
000279354 650_2 $$2MeSH$$aAlzheimer Disease: diagnosis
000279354 650_2 $$2MeSH$$aAmyloid beta-Peptides
000279354 650_2 $$2MeSH$$atau Proteins
000279354 650_2 $$2MeSH$$aModels, Statistical
000279354 650_2 $$2MeSH$$aRegression Analysis
000279354 650_2 $$2MeSH$$aComputer Simulation
000279354 7001_ $$00000-0002-5072-5347$$aKlein, Nadja$$b1
000279354 7001_ $$0P:(DE-2719)2000057$$aWagner, Michael$$b2$$udzne
000279354 7001_ $$0P:(DE-2719)2811245$$aSchmid, Matthias$$b3$$udzne
000279354 773__ $$0PERI:(DE-600)2001539-2$$a10.1177/09622802241313293$$gVol. 34, no. 5, p. 968 - 985$$n5$$p968 - 985$$tStatistical methods in medical research$$v34$$x0962-2802$$y2025
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