Copy For Citation
Dil E., KARASOY D.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.196, 2020 (SCI-Expanded)
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Publication Type:
Article / Article
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Volume:
196
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Publication Date:
2020
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Doi Number:
10.1016/j.cmpb.2020.105612
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Journal Name:
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
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Journal Indexes:
Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE
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Keywords:
gsem, Parametric joint modelling, Stata, Longitudinal data, Survival data, PROPORTIONAL HAZARDS, LIKELIHOOD APPROACH, SURVIVAL
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TED University Affiliated:
Yes
Abstract
Background: The number of studies using joint modelling of longitudinal and survival data have increased in the past two decades, but analytical techniques and software shortcomings have remained. A joint model is often used for analysis of a combination of longitudinal sub-model and survival sub-model using shared random effects. Cox regression commonly referring to the survival sub-model, should not be used when proportional hazards assumptions are not satisfied. In such cases, the parametric survival model is preferable.