Joint modeling of longitudinal continuous, longitudinal ordinal, and time-to-event outcomes

DOI10.1007/S10985-020-09511-3OpenAlexW3106749912WikidataQ102381206 ScholiaQ102381206MaRDI QIDQ825203

Khurshid Alam, Arnab Maity, Dimitris Rizopoulos, Abdus Sattar, Sanjoy Kumar Sinha

Publication date: 17 December 2021

Published in: Lifetime Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10985-020-09511-3

zbMATH Keywords

proportional odds modelfrailty modellinear mixed modeljoint modelsassociation parameters

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Survival analysis and censored data (62Nxx)

Related Items (2)

A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data ⋮ Bayesian semiparametric joint model of multivariate longitudinal and survival data with dependent censoring

Cites Work

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• Bayesian functional joint models for multivariate longitudinal and time-to-event data
• Models for discrete longitudinal data.
• A class of joint models for multivariate longitudinal measurements and a binary event
• Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data
• A Two-Part Joint Model for the Analysis of Survival and Longitudinal Binary Data with Excess Zeros

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