Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/70501
Título: | Adjusting ROC curve for Covariates with AROC R package |
Autor(es): | Costa, Francisco Machado e Braga, A. C. |
Palavras-chave: | Receiver operator characteristic curve Covariate adjustment Diagnostic test Biostatistics Software tool |
Data: | 2020 |
Editora: | Springer |
Revista: | Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Citação: | Machado e Costa F., Braga A.C. (2020) Adjusting ROC Curve for Covariates with AROC R Package. In: Gervasi O. et al. (eds) Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12251. Springer, Cham. https://doi.org/10.1007/978-3-030-58808-3_15 |
Resumo(s): | 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. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/70501 |
ISBN: | 978-3-030-58807-6 |
DOI: | 10.1007/978-3-030-58808-3_15 |
ISSN: | 0302-9743 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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ICCSA2020_artigo_FranciscoCosta.pdf | Accepted Manuscript | 377,07 kB | Adobe PDF | Ver/Abrir |