On Estimating the Relationship between Longitudinal Measurements and Time-to-Event Data Using a Simple Two-Stage Procedure

DOI10.1111/j.1541-0420.2009.01324_1.xzbMath1202.62143OpenAlexW2148060805WikidataQ33694765 ScholiaQ33694765MaRDI QIDQ3064295

Joanna H. Shih, Paul S. Albert

Publication date: 21 December 2010

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2009.01324_1.x

zbMATH Keywords

two-stage modelsjoint modelregression calibrationinformative dropout

Mathematics Subject Classification ID

Related Items (12)

A joint model of binary and longitudinal data with non-ignorable missingness, with application to marital stress and late-life major depression in women ⋮ Jointly modelling multiple transplant outcomes by a competing risk model via functional principal component analysis ⋮ Simultaneous variable selection for joint models of longitudinal and survival outcomes ⋮ Simultaneous Bayesian modeling of longitudinal and survival data in breast cancer patients ⋮ Bayesian inference on longitudinal-survival data with multiple features ⋮ Joint models for longitudinal and discrete survival data in credit scoring ⋮ Jointly modeling skew longitudinal survival data with missingness and mismeasured covariates ⋮ Two-step and likelihood methods for joint models of longitudinal and survival data ⋮ A modified two-stage approach for joint modelling of longitudinal and time-to-event data ⋮ Joint models for multiple longitudinal processes and time-to-event outcome ⋮ Analysis of longitudinal and survival data: joint modeling, inference methods, and issues ⋮ A joint model for repeated events of different types and multiple longitudinal outcomes with application to a follow‐up study of patients after kidney transplant

Cites Work

• Unnamed Item
• Random-Effects Models for Longitudinal Data
• A Semiparametric Likelihood Approach to Joint Modeling of Longitudinal and Time-to-Event Data
• Estimation and comparison of Changes in the Presence of Informative Right Censoring: Conditional Linear Model
• Semiparametric Modeling of Longitudinal Measurements and Time‐to‐Event Data–A Two‐Stage Regression Calibration Approach
• On errors-in-variables for binary regression models
• Semiparametric Stochastic Mixed Models for Longitudinal Data
• Use of Summary Measures to Adjust for Informative Missingness in Repeated Measures Data with Random Effects
• Joint Modeling of Survival and Longitudinal Data: Likelihood Approach Revisited

This page was built for publication: On Estimating the Relationship between Longitudinal Measurements and Time-to-Event Data Using a Simple Two-Stage Procedure