Bayesian Bootstrap Inference for the ROC Surface

Autor: de Carvalho, Vanda Inacio, de Carvalho, Miguel, Branscum, Adam

Accurate diagnosis of disease is of great importance in clinical practice and medical research. The receiver operating characteristic (ROC) surface is a popular tool for evaluating the discriminatory ability of continuous diagnostic test outcomes whe

Externí odkaz: http://arxiv.org/abs/1805.07622

FUNCTIONAL COVARIATE-ADJUSTED PARTIAL AREA UNDER THE SPECIFICITY-ROC CURVE WITH AN APPLICATION TO METABOLIC SYNDROME DIAGNOSIS

Autor: de Carvalho, Vanda Inácio, de Carvalho, Miguel, Alonzo, Todd A., González-Manteiga, Wenceslao

Publikováno v: The Annals of Applied Statistics, 2016 Sep 01. 10(3), 1472-1495.

Externí odkaz: http://www.jstor.org/stable/43956889

Statistical Evaluation of Medical Tests

Autor: Vanda Inacio, Pilar Gayoso-Diz, María Xosé Rodríguez-Álvarez

Publikováno v: Annual Review of Statistics and Its Application. 8:41-67

In this review, we present an overview of the main aspects related to the statistical evaluation of medical tests for diagnosis and prognosis. Measures of diagnostic performance for binary tests, such as sensitivity, specificity, and predictive value

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::762f7de1d8fa715c9ee9af3371c00b75
https://doi.org/10.1146/annurev-statistics-040720-022432

Parameter clustering in Bayesian functional principal component analysis of neuroscientific data

Autor: Vanda Inacio, Nicolò Margaritella, Ruth King

Publikováno v: Statistics in medicineReferences. 40(1)

The extraordinary advancements in neuroscientific technology for brain recordings over the last decades have led to increasingly complex spatiotemporal data sets. To reduce oversimplifications, new models have been developed to be able to identify me

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::330ed0d0805c55d7779c79c681bfb687
https://pubmed.ncbi.nlm.nih.gov/33040367

Bayesian semiparametric modeling for HIV longitudinal data with censoring and skewness

Autor: Victor H. Lachos, Cristian L. Bayes, Luis M. Castro, Wan-Lun Wang, Vanda Inacio de Carvalho

Publikováno v: Castro, L M, Wang, W-L, Lachos, V H, Calhau Fernandes Inacio De Carvalho, V & Bayes, C L 2019, ' Bayesian semiparametric modeling for HIV longitudinal data with censoring and skewness ', Statistical Methods in Medical Research, vol. 28, no. 5, pp. 1457-1476 . https://doi.org/10.1177/0962280218760360

In biomedical studies, the analysis of longitudinal data based on Gaussian assumptions is common practice. Nevertheless, more often than not, the observed responses are naturally skewed, rendering the use of symmetric mixed effects models inadequate.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d502905c0e6026099982411ee6285d1
https://www.pure.ed.ac.uk/ws/files/57625579/SkewNormalCenSemiPar.pdf