e-learning
resources
ERJ
Login
Search all ERS
e-learning
resources
Disease Areas
Airways Diseases
Interstitial Lung Diseases
Respiratory Critical Care
Respiratory Infections
Paediatric Respiratory Diseases
Pulmonary Vascular Diseases
Sleep and Breathing Disorders
Thoracic Oncology
Events
International Congress
Courses
Webinars
Conferences
Research Seminars
Journal Clubs
Publications
Breathe
Monograph
ERJ
ERJ Open Research
ERR
European Lung White Book
Handbook Series
Guidelines
All ERS guidelines
e-learning
CME Online
Case reports
Short Videos
SpirXpert
Procedure Videos
CME tests
Reference Database of Respiratory Sounds
Radiology Image Challenge
Brief tobacco interventions
EU Projects
VALUE-Dx
ERN-LUNG
ECRAID
UNITE4TB
Disease Areas
Events
Publications
Guidelines
e-learning
EU Projects
Login
Search
Using joint modelling to assess the association between a time-varying biomarker and a survival outcome: an illustrative example in respiratory medicine
Yuntao Chen, Douwe Postmus, Martin R. Cowie, Holger Woehrle, Karl Wegscheider, Anita K. Simonds, Marike Boezen, Virend K. Somers, Helmut Teschler, Christine Eulenburg
Source:
Eur Respir J, 57 (2) 2003206; 10.1183/13993003.03206-2020
Journal Issue:
February
Rating:
You must
login
to grade this presentation.
Share or cite this content
Citations should be made in the following way:
Yuntao Chen, Douwe Postmus, Martin R. Cowie, Holger Woehrle, Karl Wegscheider, Anita K. Simonds, Marike Boezen, Virend K. Somers, Helmut Teschler, Christine Eulenburg. Using joint modelling to assess the association between a time-varying biomarker and a survival outcome: an illustrative example in respiratory medicine. Eur Respir J, 57 (2) 2003206; 10.1183/13993003.03206-2020
You must
login
to share this Presentation/Article on Twitter, Facebook, LinkedIn or by email.
Member's Comments
No comment yet.
You must
Login
to comment this presentation.
Related content which might interest you:
Panel discussion: Diagnostic tools for Obstructive Sleep Apnoea in adults and children
Expert interview: Physiological classification of lung function impairment
Occupational exposures to respiratory diseases: A case-based discussion
Related content which might interest you:
Is it possible to predict asthma outcomes? A disease model for economic analysis.
Source: International Congress 2017 – Asthma management
Year: 2017
Features of asthma management: quantifying the patient‘s perspective using discrete choice modelling
Source: Eur Respir J 2006; 28: Suppl. 50, 122s
Year: 2006
Multicomponent indices to predict survival in COPD: the COCOMICS study
Source: Eur Respir J 2013; 42: 323-332
Year: 2013
Which clinical variables best predict asthma control?
Source: Annual Congress 2010 - Asthma: breath biomarkers and asthma control
Year: 2010
A basic prediction model with clinical parameters to diagnose lung cancer.
Source: Virtual Congress 2021 – Screening, diagnosis, management and prognosis of lung cancer
Year: 2021
IPF cluster analysis highlights diagnostic delay and cardiovascular comorbidities association with outcome
Source: Virtual Congress 2021 – Interstitial lung disease around the world
Year: 2021
Introducing a new prognostic instrument for long-term mortality prediction among COPD patients - the CADOT index.
Source: International Congress 2019 – Advances in clinical management of COPD
Year: 2019
COPD related fatigue (COPD-RF) as a patient centred outcome tool in COPD: Various predictors and its correlation with other outcome parameters
Source: International Congress 2016 – Monitoring airway diseases with clinical tools
Year: 2016
Cross-sectional biomarker comparisons with a longitudinal perspective in asthma monitoring: the eNose premise
Source: Virtual Congress 2020 – New tools for diagnosis of obstructive diseases
Year: 2020
A practical measurement of thoracic sarcopenia: correlation with clinical parameters and outcomes in advanced lung cancer
Source: ERJ Open Res 2016: 00085-2015
Year: 2016
Utilising biomarkers to predict right heart maladaptive phenotype: a step toward precision medicine
Source: Eur Respir J, 57 (4) 2004506; 10.1183/13993003.04506-2020
Year: 2021
Development of a tool to measure the clinical response to biologic therapy in uncontrolled severe asthma: the FEOS score.
Source: Virtual Congress 2021 – Severe asthma: evaluation using patient-reported outcome measures (PROMs) and biomarkers, comorbidities and treatments
Year: 2021
Does a predominant clinical COPD phenotype predict different outcome responses to pulmonary rehabilitation?
Source: Annual Congress 2012 - The best posters in pulmonary rehabilitation and chronic care
Year: 2012
Different definitions in childhood asthma: how dependable is the dependent variable?
Source: Eur Respir J 2010; 36: 48-56
Year: 2010
Comparison of multidimensional assessment systems with regard to risk prediction for exacerbations of COPD
Source: Annual Congress 2011 - COPD exacerbation
Year: 2011
External validation of the French predictive model to estimate PAH survival: A REVEAL analysis
Source: Annual Congress 2012 - Pulmonary circulation: clinical databases and registries
Year: 2012
A general framework to implement predictive models for hospitalizations: Application to COPD
Source: International Congress 2017 – Best abstracts in the management of chronic respiratory diseases
Year: 2017
Evaluating COPD disease management: application of a theoretical model
Source: Annual Congress 2008 - Issues in the management of COPD in the community
Year: 2008
Applicability of pulmonary rehabilitation in primary care: critical success factors, use of patient reported outcomes and consequences of population differences
Source: Annual Congress 2011 - Primary Care Day I: Effective disease- and self-management in primary care
Year: 2011
An innovative risk score for prediction of asthma related adverse outcome
Source: Annual Congress 2009 - Quality of treatment in primary respiratory care
Year: 2009
We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking "Accept", you consent to the use of the cookies.
Accept