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Prediction of coronary artery disease risk based on multiple longitudinal biomarkers.

Prediction of coronary artery disease risk based on multiple longitudinal biomarkers.

http://www.wikidata.org/entity/Q37257959

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Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis.Dynamics of biomarkers in relation to aging and mortality.Joint Models for Multiple Longitudinal Processes and Time-to-event Outcome Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer's disease.Functional joint model for longitudinal and time-to-event data: an application to Alzheimer's disease.DYNAMIC PREDICTION FOR MULTIPLE REPEATED MEASURES AND EVENT TIME DATA: AN APPLICATION TO PARKINSON'S DISEASE.The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study.

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Prediction of coronary artery disease risk based on multiple longitudinal biomarkers.

http://www.wikidata.org/entity/Q37257959

description

article científic

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article scientifique

@fr

articolo scientifico

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artigo científico

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bilimsel makale

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scientific article published on 05 October 2015

@en

vedecký článok

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vetenskaplig artikel

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videnskabelig artikel

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vědecký článek

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name

Prediction of coronary artery disease risk based on multiple longitudinal biomarkers.

@en

Prediction of coronary artery disease risk based on multiple longitudinal biomarkers.

@nl

type

label

Prediction of coronary artery disease risk based on multiple longitudinal biomarkers.

@en

Prediction of coronary artery disease risk based on multiple longitudinal biomarkers.

@nl

prefLabel

Prediction of coronary artery disease risk based on multiple longitudinal biomarkers.

@en

Prediction of coronary artery disease risk based on multiple longitudinal biomarkers.

@nl

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Statistics in Medicine

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Prediction of coronary artery disease risk based on multiple longitudinal biomarkers.

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Lili Yang

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Menggang Yu

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On estimating the relationship between longitudinal measurements and time-to-event data using a simple two-stage procedure.

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Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data.

Life course trajectories of systolic blood pressure using longitudinal data from eight UK cohorts.

Bayesian inference on joint models of HIV dynamics for time-to-event and longitudinal data with skewness and covariate measurement errors.

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1299-1314

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scholarly article

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10.1002/SIM.6754

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26439685

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