Bootstrap-based procedures for inference in nonparametric receiver-operating characteristic curve regression analysis

Rodríguez‐Álvarez, María Xosé; Roca‐Pardiñas, Javier; Cadarso‐Suárez, Carmen; Tahoces, Pablo G.

doi:10.1177/0962280217742542

Cited by 8 publications

(3 citation statements)

References 37 publications

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“…Specifically, we used the non-parametric direct and induced approach proposed by Rodriguez-Alvarez et al . 42 . The direct method models the ROC curve using generalized additive models (ROC-GAM) 42,43 .…”

Section: Methodsmentioning

confidence: 99%

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Visceral adiposity index (VAI), lipid accumulation product (LAP), and product of triglycerides and glucose (TyG) to discriminate prediabetes and diabetes

Ahn

Baumeister

Amann

et al. 2019

Sci Rep

154

106

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The present study evaluated the ability of the visceral adiposity index (VAI), the lipid accumulation product (LAP), and product of triglycerides and glucose (TyG), three novel, insulin resistance-related markers, to discriminate prediabetes/diabetes in the general German population. Altogether 2,045 Germans (31–72 years, 53.3% women) without known diabetes and a history of Myocardial Infarction (MI)/stroke from the Cooperative Health Research in the Region of Augsburg (KORA) F4 Study were eligible. The discriminatory accuracy of the markers for oral glucose tolerance test (OGTT)-defined prediabetes/diabetes according to the American Diabetes Association (ADA) criteria was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). The Youden Index (YI) was used to determine optimal cut-off values, and a non-parametric ROC regression was used to examine whether the discriminatory accuracy varied by sex and age. 365 men (38.2%) and 257 women (23.6%) were newly diagnosed with prediabetes/diabetes. AUCs for TyG, LAP and VAI were 0.762 (95% CI 0.740–0.784), 0.743 (95% CI 0.720–0.765), and 0.687 (95% CI 0.662–0.712), respectively. The optimal cut-off values for the LAP and TyG were 56.70 and 8.75 in men, and 30.40 and 8.53 in women. In conclusion, TyG and LAP provide good discrimination of persons with prediabetes/diabetes.

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Section: Methodsmentioning

confidence: 99%

“…42 . The direct method models the ROC curve using generalized additive models (ROC-GAM) 42,43 . It allowed direct evaluations of the impact of age on the ROC curve and tests for an interaction between age and sex.…”

Section: Methodsmentioning

confidence: 99%

Visceral adiposity index (VAI), lipid accumulation product (LAP), and product of triglycerides and glucose (TyG) to discriminate prediabetes and diabetes

Ahn

Baumeister

Amann

et al. 2019

Sci Rep

154

106

View full text Add to dashboard Cite

show abstract

“…Nevertheless, there are not many methods in the literature capable of considering more than one covariate when estimating the conditional ROC curve, and most of them have some parametric assumptions that we would like to avoid making. See Inácio de Carvalho et al (2013) as an example of a nonparametric Bayesian model to estimate the conditional distribution functions involved in the ROC curves, Rodríguez-Álvarez et al (2011) or Rodríguez-Álvarez et al (2018) as an example of a direct ROC regression model or Rodríguez and Martínez (2014) as an example of induced methodology (framed in a Bayesian setting). In our case we will be following a frequentist approach.…”

Section: Introductionmentioning

confidence: 99%

A test for comparing conditional ROC curves with multidimensional covariates

Fanjul-Hevia

Pardo–Fernández

Keilegom

et al. 2022

Journal of Applied Statistics

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The comparison of Receiver Operating Characteristic (ROC) curves is frequently used in the literature to compare the discriminatory capability of different classification procedures based on diagnostic variables. The performance of these variables can be sometimes influenced by the presence of other covariates, and thus they should be taken into account when making the comparison. A new non-parametric test is proposed here for testing the equality of two or more dependent ROC curves conditioned to the value of a multidimensional covariate. Projections are used for transforming the problem into a one-dimensional approach easier to handle. Simulations are carried out to study the practical performance of the new methodology. A real data set of patients with Pleural Effusion is analysed to illustrate this procedure.

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Adjusting ROC Curve for Covariates with AROC R Package

Costa

Braga

2020

Lecture Notes in Computer Science

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The ability of a medical test to differentiate between diseased and non-diseased states is of vital importance and must be screened by statistical analysis for reliability and improvement. The receiver operating characteristic (ROC) curve remains a popular method of marker analysis, disease screening and diagnosis. Covariates in this field related to the subject’s characteristics are incorporated in the analysis to avoid bias. The covariate adjusted ROC (AROC) curve was proposed as a method of incorporation. The AROC R-package was recently released and brings various methods of estimation based on multiple authors work. The aim of this study was to explore the AROC package functionality and usability using real data noting its possible limitations. The main methods of the package were capable of incorporating different and multiple variables, both categorical and continuous, in the AROC curve estimation. When tested for the same data, AROC curves are generated with no statistical differences, regardless of method. The package offers a variety of methods to estimate the AROC curve complemented with predictive checks and pooled ROC estimation. The package offers a way to conduct a more thorough ROC and AROC analysis, making it available for any R user.

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Bootstrap-based procedures for inference in nonparametric receiver-operating characteristic curve regression analysis

Cited by 8 publications

References 37 publications

Visceral adiposity index (VAI), lipid accumulation product (LAP), and product of triglycerides and glucose (TyG) to discriminate prediabetes and diabetes

Visceral adiposity index (VAI), lipid accumulation product (LAP), and product of triglycerides and glucose (TyG) to discriminate prediabetes and diabetes

A test for comparing conditional ROC curves with multidimensional covariates

Adjusting ROC Curve for Covariates with AROC R Package

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