0000000000589795

AUTHOR

Marisa Baré

showing 3 related works from this author

An Ordinal Joint Model for Breast Cancer

2017

We propose a Bayesian joint model to analyze the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional-odds cumulative logit model and the time-to-event process through a left-truncated Cox proportional hazards model with information of the longitudinal marker and baseline covariates. Both longitudinal and survival processes are connected by a common vector of random effects.

Oncologymedicine.medical_specialtyProportional hazards modelComputer scienceBayesian probabilityPosterior probabilityMarkov chain Monte CarloRandom effects modelmedicine.diseasesymbols.namesakeBreast cancerInternal medicineCovariateStatisticsmedicinesymbolsEvent (probability theory)
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Bayesian joint ordinal and survival modeling for breast cancer risk assessment

2016

We propose a joint model to analyze the structure and intensity of the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional-odds cumulative logit model. Time-to-event is modeled through a left-truncated proportionalhazards model, which incorporates information of the longitudinal marker as well as baseline covariates. Both longitudinal and survival processes are connected by means of a common vector of random effects. General inferences are discussed under the Bayesian approach and include the posterior distribution of the probabilities associated to each longitudinal category and the …

Statistics and ProbabilityEpidemiologyComputer scienceBreast imagingLeft-truncated proportional-hazards modelBayesian probabilityPosterior probabilityPopulationBreast Neoplasmsleft‐truncated proportional‐hazards modelRisk Assessment:Matemàtiques i estadística::Investigació operativa [Àrees temàtiques de la UPC]01 natural sciences010104 statistics & probability03 medical and health sciencesBayes' theorem0302 clinical medicineBreast cancerStatisticsCovariateEconometricsmedicineHumansBreast0101 mathematicseducationResearch ArticlesBI-RADS scaleBreast Densityeducation.field_of_studyBI‐RADS scaleLatent processBayes TheoremRandom effects modelmedicine.disease:90 Operations research mathematical programming [Classificació AMS]030220 oncology & carcinogenesisProportional‐odds cumulative logit modelFemaleProportional-odds cumulative logit modelResearch ArticleStatistics in Medicine
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Factors that influence treatment delay in patients with colorectal cancer

2016

// Irene Zarcos-Pedrinaci 1, 11 , Alberto Fernandez-Lopez 2 , Teresa Tellez 1, 11 , Francisco Rivas-Ruiz 1, 11 , Antonio Rueda A 3, 11 , Maria Manuela Morales Suarez-Varela 4 , Eduardo Briones 5 , Marisa Bare 6, 11 , Antonio Escobar 7, 11 , Cristina Sarasqueta 8, 11 , Nerea Fernandez de Larrea 9, 11 , Urko Aguirre 10, 11 , Jose Maria Quintana 10, 11 , Maximino Redondo 1, 11 and On Behalf of the CARESS-CCR Study Group 1 Research Unit, Agencia Sanitaria Costa del Sol, Marbella, Spain 2 Servicio de Cirugia, Agencia Sanitaria Costa del Sol, Marbella, Spain 3 Servicio de Oncologia Medica, Agencia Sanitaria Costa del Sol, Marbella, Spain 4 Unit of Public Health, Hygiene and Environmental Health, …

GerontologyMaleDelayed Diagnosis0302 clinical medicineHygieneRisk FactorsEpidemiologyCancer screeningOdds RatioNeoplasm MetastasisProspective cohort studyCàncerColorectalmedia_commonCancercolorectaleducationDelaytreatmentMiddle AgedOncology030220 oncology & carcinogenesisPopulation study030211 gastroenterology & hepatologyFemaleColorectal Neoplasmsmedicine.medical_specialtydelaymedia_common.quotation_subjectEducationTime-to-Treatment03 medical and health sciencesmedicineBiomarkers TumorcancerHumansRecte MalaltiesPreventive healthcareAgedNeoplasm Stagingbusiness.industryPublic healthTreatmentSocioeconomic FactorsFamily medicineClinical Research PaperNeoplasm GradingbusinessFactor Analysis StatisticalEnvironmental epidemiology
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