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Review and Comparison of Computational Approaches for Joint Longitudinal and Time‐to‐Event Models
- Source :
- Int Stat Rev
- Publication Year :
- 2019
- Publisher :
- Wiley, 2019.
-
Abstract
- Joint models for longitudinal and time-to-event data are useful in situations where an association exists between a longitudinal marker and an event time. These models are typically complicated due to the presence of shared random effects and multiple submodels. As a consequence, software implementation is warranted that is not prohibitively time consuming. While methodological research in this area continues, several statistical software procedures exist to assist in the fitting of some joint models. We review the available implementation for frequentist and Bayesian models in the statistical programming languages R, SAS, and Stata. A description of each procedure is given including estimation techniques, input and data requirements, available options for customization, and some available extensions, such as competing risks models. The software implementations are compared and contrasted through extensive simulation, highlighting their strengths and weaknesses. Data from an ongoing trial on adrenal cancer patients is used to study different nuances of software fitting on a practical example.
- Subjects :
- Statistics and Probability
Event (computing)
Computer science
business.industry
Association (object-oriented programming)
05 social sciences
Bayesian probability
Machine learning
computer.software_genre
Random effects model
01 natural sciences
Article
Personalization
010104 statistics & probability
Software
Frequentist inference
0502 economics and business
Artificial intelligence
0101 mathematics
Statistics, Probability and Uncertainty
business
computer
Strengths and weaknesses
050205 econometrics
Subjects
Details
- ISSN :
- 17515823 and 03067734
- Database :
- OpenAIRE
- Journal :
- International Statistical Review
- Accession number :
- edsair.doi.dedup.....f76d27adcde4ddcaa4403fecc27e28bd