Two-step and likelihood methods for joint models of longitudinal and survival data

DOI10.1080/03610918.2016.1188208zbMath1388.62296OpenAlexW2530328900MaRDI QIDQ4638799

Lang Wu, Qian Ye

Publication date: 30 April 2018

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2016.1188208

zbMATH Keywords

EM algorithmapproximation methodmixed effects modeltwo-step methodslikelihood method

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Estimation in survival analysis and censored data (62N02) Reliability and life testing (62N05)

Uses Software

• JM

Cites Work

• The joint model of the logistic model and linear random effect model -- an application to predict orthostatic hypertension for subacute stroke patients
• An approach for jointly modeling multivariate longitudinal measurements and discrete time-to-event data
• Accuracy of Laplace approximation for discrete response mixed models
• Joint Models for Longitudinal and Time-to-Event Data
• On Estimating the Relationship between Longitudinal Measurements and Time-to-Event Data Using a Simple Two-Stage Procedure
• Semiparametric Modeling of Longitudinal Measurements and Time‐to‐Event Data–A Two‐Stage Regression Calibration Approach
• Covariate measurement errors and parameter estimation in a failure time regression model
• A Joint Model for Survival and Longitudinal Data Measured with Error
• Conditional Second-Order Generalized Estimating Equations for Generalized Linear and Nonlinear Mixed-Effects Models
• Approximate Inference in Generalized Linear Mixed Models
• Joint Modeling of Survival and Longitudinal Data: Likelihood Approach Revisited

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