Longitudinal data analysis for generalized linear models with follow-up dependent on outcome-related variables

DOI10.1002/CJS.5550350402zbMath1143.62041OpenAlexW1993466235MaRDI QIDQ3512628

Thomas Lumley, Petra Bůžková

Publication date: 21 July 2008

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

Full work available at URL: https://doi.org/10.1002/cjs.5550350402

zbMATH Keywords

longitudinal datacounting processgeneralized estimating equationtime-varying covariatesoutcome-dependent follow-up

Mathematics Subject Classification ID

Non-Markovian processes: estimation (62M09) Generalized linear models (logistic models) (62J12) Estimation in survival analysis and censored data (62N02)

Related Items (7)

Mixed‐effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study ⋮ Robust estimation for longitudinal data under outcome‐dependent visit processes ⋮ Meeting the assumptions of inverse-intensity weighting for longitudinal data subject to irregular follow-up: suggestions for the design and analysis of clinic-based cohort studies ⋮ Time‐Varying Latent Effect Model for Longitudinal Data with Informative Observation Times ⋮ Quantile regression analysis of censored longitudinal data with irregular outcome-dependent follow-up ⋮ Semiparametric log-linear regression for longitudinal measurements subject to outcome-depen\-dent follow-up ⋮ Time to Diagnosis: Accounting for Differential Endpoint Follow-up in Multi-Cohort Studies

Uses Software

• Stata
• Unnamed Item

Cites Work

• Unnamed Item
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• Semiparametric and Nonparametric Regression Analysis of Longitudinal Data
• Analysis of Longitudinal Data with Irregular, Outcome-Dependent Follow-Up
• Informative Drop-Out in Longitudinal Data Analysis
• Measurement Error in Nonlinear Models

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