Jointly modelling multiple transplant outcomes by a competing risk model via functional principal component analysis

DOI10.1080/02664763.2021.1981256OpenAlexW3201826399MaRDI QIDQ5058223

No author found.

Publication date: 19 December 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2021.1981256

zbMATH Keywords

latent variablescompeting risksjoint modelfunctional principal component analysiskidney transplant

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Uses Software

• cmprsk

Cites Work

• Unnamed Item
• Unnamed Item
• An approach for jointly modeling multivariate longitudinal measurements and discrete time-to-event data
• On Estimating the Relationship between Longitudinal Measurements and Time-to-Event Data Using a Simple Two-Stage Procedure
• Modeling Longitudinal Data with Nonparametric Multiplicative Random Effects Jointly with Survival Data
• Semiparametric Modeling of Longitudinal Measurements and Time‐to‐Event Data–A Two‐Stage Regression Calibration Approach
• The Analysis of Failure Times in the Presence of Competing Risks
• Principal component models for sparse functional data
• A Proportional Hazards Model for the Subdistribution of a Competing Risk
• A modified two-stage approach for joint modelling of longitudinal and time-to-event data
• Functional Data Analysis for Sparse Longitudinal Data

This page was built for publication: Jointly modelling multiple transplant outcomes by a competing risk model via functional principal component analysis