Modelling time-varying covariates effect on survival via functional data analysis: application to the MRC BO06 trial in osteosarcoma
From MaRDI portal
Publication:6163507
DOI10.1007/s10260-022-00647-0OpenAlexW4281974532MaRDI QIDQ6163507
Francesca Ieva, Marta Spreafico, Marta Fiocco
Publication date: 26 June 2023
Published in: Statistical Methods and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10260-022-00647-0
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A k-means procedure based on a Mahalanobis type distance for clustering multivariate functional data
- BFLCRM: a Bayesian functional linear Cox regression model for predicting time to conversion to Alzheimer's disease
- Optimal estimation for the functional Cox model
- Modeling survival data: extending the Cox model
- Applied functional data analysis. Methods and case studies
- Functional data analysis.
- Nonparametric functional data analysis. Theory and practice.
- Joint Models for Longitudinal and Time-to-Event Data
- FLCRM: Functional linear cox regression model
- Joint model with latent state for longitudinal and multistate data
- Identifying temporally differentially expressed genes through functional principal components analysis
- Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains
- Cox regression models with functional covariates for survival data
- Functional Modelling and Classification of Longitudinal Data*
- Joint Models for Multivariate Longitudinal and Multivariate Survival Data
- Joint modelling of longitudinal measurements and event time data
- Survival Model Predictive Accuracy and ROC Curves
- Functional Data Analysis for Sparse Longitudinal Data
- Functional modeling of recurrent events on time‐to‐event processes
This page was built for publication: Modelling time-varying covariates effect on survival via functional data analysis: application to the MRC BO06 trial in osteosarcoma
