Modeling Repeated Count Data Subject to Informative Dropout

DOI10.1111/j.0006-341X.2000.00667.xzbMath1060.62573OpenAlexW2060793194WikidataQ30612509 ScholiaQ30612509MaRDI QIDQ4670397

Paul S. Albert, Dean A. Follmann

Publication date: 22 April 2005

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.0006-341x.2000.00667.x

zbMATH Keywords

generalized estimating equationsgeneralized linear mixed modelsnonignorable missingnessshared random effectsapproximate conditional model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items (16)

Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects ⋮ Mixtures of Varying Coefficient Models for Longitudinal Data with Discrete or Continuous Nonignorable Dropout ⋮ A joint model of binary and longitudinal data with non-ignorable missingness, with application to marital stress and late-life major depression in women ⋮ A Bayesian shared parameter model for joint modeling of longitudinal continuous and binary outcomes ⋮ Generalized linear mixed joint model for longitudinal and survival outcomes ⋮ Penalized likelihood approach for simultaneous analysis of survival time and binary longitudinal outcome ⋮ Joint modeling of survival time and longitudinal outcomes with flexible random effects ⋮ Alternating imputation posterior estimation of models with crossed random effects ⋮ Marginalized transition shared random effects models for longitudinal binary data with nonignorable dropout ⋮ Improved regression calibration ⋮ A Global Sensitivity Test for Evaluating Statistical Hypotheses with Nonidentifiable Models ⋮ Regression analysis of interval-censored failure time data with time-dependent covariates ⋮ A Latent Autoregressive Model for Longitudinal Binary Data Subject to Informative Missingness ⋮ A Dirichlet process mixture model for non-ignorable dropout ⋮ Meta‐Analysis of Studies with Missing Data ⋮ Estimation in Longitudinal or Panel Data Models with Random‐Effect‐Based Missing Responses

Uses Software

• GAUSS

Cites Work

• Longitudinal data analysis using generalized linear models
• Estimation and comparison of Changes in the Presence of Informative Right Censoring: Conditional Linear Model
• Models for Longitudinal Data: A Generalized Estimating Equation Approach
• Correlated Binary Regression with Covariates Specific to Each Binary Observation
• Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modeling the Censoring Process
• Accurate Approximations for Posterior Moments and Marginal Densities
• Model for the Analysis of Binary Longitudinal Pain Data Subject to Informative Dropout through Remedication
• Semiparametric Regression for Repeated Outcomes with Nonignorable Nonresponse
• Mixed Effects Logistic Regression Models for Longitudinal Binary Response Data with Informative Drop-Out
• An Approximate Generalized Linear Model with Random Effects for Informative Missing Data
• Modeling the Drop-Out Mechanism in Repeated-Measures Studies

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