A Transitional Model for Longitudinal Binary Data Subject to Nonignorable Missing Data

DOI10.1111/j.0006-341X.2000.00602.xzbMath1060.62572OpenAlexW2076719829WikidataQ30600500 ScholiaQ30600500MaRDI QIDQ4667489

Paul S. Albert

Publication date: 20 April 2005

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.0006-341x.2000.00602.x

zbMATH Keywords

EM algorithmhidden Markov modelsMarkov modelsmissing datarepeated binary dataepisodic dataopiate addition

Mathematics Subject Classification ID

Related Items (14)

Joint modeling of recurrent event processes and intermittently observed time-varying binary covariate processes ⋮ Intermittent Missing Observations in Discrete-Time Hidden Markov Models ⋮ Analysis of ordinal outcomes with longitudinal covariates subject to missingness ⋮ A latent transition analysis model for latent-state-dependent nonignorable missingness ⋮ A discrete time event‐history approach to informative drop‐out in mixed latent Markov models with covariates ⋮ Transitional modeling of experimental longitudinal data with missing values ⋮ A Brief Review of Approaches to Non‐ignorable Non‐response ⋮ A Two-Part Joint Model for the Analysis of Survival and Longitudinal Binary Data with Excess Zeros ⋮ An exploration of fixed and random effects selection for longitudinal binary outcomes in the presence of nonignorable dropout ⋮ Modified weights based generalized quasilikelihood inferences in incomplete longitudinal binary models ⋮ Dynamic Analysis of Recurrent Event Data with Missing Observations, with Application to Infant Diarrhoea in Brazil ⋮ Hidden Markov models for alcoholism treatment trial data ⋮ Progressive multi-state models for informatively incomplete longitudinal data ⋮ A Latent Autoregressive Model for Longitudinal Binary Data Subject to Informative Missingness

Cites Work

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• Markov Regression Models for Time Series: A Quasi-Likelihood Approach
• Influence of Human Immunodeficiency Virus Infection on Neurological Impairment: An Analysis of Longitudinal Binary Data with Informative Drop-Out
• Stochastic Algorithms for Markov Models Estimation with Intermittent Missing Data
• Informative Drop-Out in Longitudinal Data Analysis
• Non-Ignorable Non-Response Models for Time-Ordered Categorical Variables
• A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains

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