Joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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1471-2288
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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online resource.
- Bibliographic citation
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Joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis ; volume:16 ; number:1 ; day:5 ; month:12 ; year:2016 ; pages:1-11 ; date:12.2016
BMC medical research methodology ; 16, Heft 1 (5.12.2016), 1-11, 12.2016
- Creator
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Sudell, Maria
- Contributor
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Kolamunnage-Dona, Ruwanthi
Tudur-Smith, Catrin
SpringerLink (Online service)
- DOI
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10.1186/s12874-016-0272-6
- URN
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urn:nbn:de:1111-201701157189
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:59 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Sudell, Maria
- Kolamunnage-Dona, Ruwanthi
- Tudur-Smith, Catrin
- SpringerLink (Online service)
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Reporting of loss to follow-up information in randomised controlled trials with time-to-event outcomes: a literature survey
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joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes
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Correction to: joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis
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A joint model for nonparametric functional mapping of longitudinal trajectory and time-to-event
Reporting of loss to follow-up information in randomised controlled trials with time-to-event outcomes: a literature survey
Mediation analysis with a time-to-event outcome: a review of use and reporting in healthcare research
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Sample size formula for joint modelling of longitudinal and time-to-event data in clinical trials
Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker
joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes
Flexible Bayesian joint models for longitudinal biomarkers and time-to-event outcomes with applications to type 1 diabetes research