Cargando…
Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques
Joint models are a powerful class of statistical models which apply to any data where event times are recorded alongside a longitudinal outcome by connecting longitudinal and time-to-event data within a joint likelihood allowing for quantification of the association between the two outcomes without...
Autores principales: | Griesbach, Colin, Groll, Andreas, Bergherr, Elisabeth |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8608498/ https://www.ncbi.nlm.nih.gov/pubmed/34819988 http://dx.doi.org/10.1155/2021/4384035 |
Ejemplares similares
-
Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques
por: Griesbach, Colin, et al.
Publicado: (2021) -
Development of a Likelihood of Survival Scoring System for Hospitalized Equine Neonates Using Generalized Boosted Regression Modeling
por: Dembek, Katarzyna A., et al.
Publicado: (2014) -
Statistical modelling of survival data with random effects: h-likelihood approach
por: Ha, Il Do, et al.
Publicado: (2017) -
Empirical likelihood method in survival analysis
por: Zhou, Mai
Publicado: (2015) -
Direction of Arrival Estimation in Elliptical Models via Sparse Penalized Likelihood Approach
por: Chen, Chen, et al.
Publicado: (2019)