Latent Pattern Mixture Models for Informative Intermittent Missing Data in Longitudinal Studies

DOI10.1111/j.0006-341X.2004.00173.xzbMath1132.62362OpenAlexW2068535411WikidataQ30936743 ScholiaQ30936743MaRDI QIDQ3442957

Haiqun Lin, Robert Rosenheck, Charles E. McCulloch

Publication date: 25 May 2007

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.0006-341x.2004.00173.x

zbMATH Keywords

longitudinal datalatent classjoint analysisconditional independence assumptionintermittent missing datamental health servicesvisit process

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (16)

Bayesian latent pattern mixture models for handling attrition in panel studies with refreshment samples ⋮ Regression Analysis of Longitudinal Data with Time-Dependent Covariates and Informative Observation Times ⋮ A Bayesian data combination approach for repeated durations under unobserved missing indicators: application to interpurchase-timing in marketing ⋮ Application of seemingly unrelated regression in medical data with intermittently observed time-dependent covariates ⋮ A General Class of Pattern Mixture Models for Nonignorable Dropout with Many Possible Dropout Times ⋮ A Joint Model for Longitudinal Measurements and Survival Data in the Presence of Multiple Failure Types ⋮ A latent class selection model for nonignorably missing data ⋮ A reanalysis of a longitudinal scleroderma clinical trial using non-ignorable missingness models ⋮ Score Test for Conditional Independence Between Longitudinal Outcome and Time to Event Given the Classes in the Joint Latent Class Model ⋮ Joint modelling of multivariate longitudinal outcomes and a time-to-event: a nonlinear latent class approach ⋮ Joint Modeling and Analysis of Longitudinal Data with Informative Observation Times ⋮ Modeling Longitudinal Obesity Data with Intermittent Missingness Using a New Latent Variable Model ⋮ Starting Values for EM Estimation of Latent Class Joint Model ⋮ Initial classification of joint data in EM estimation of latent class joint model ⋮ Bayesian profiling multiple imputation for missing hemoglobin values in electronic health records ⋮ Handling attrition in longitudinal studies: the case for refreshment samples

Cites Work

• Testing the number of components in a normal mixture
• Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable
• A Latent Autoregressive Model for Longitudinal Binary Data Subject to Informative Missingness
• Modeling Longitudinal Data with Nonignorable Dropouts Using a Latent Dropout Class Model
• Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modeling the Censoring Process
• Latent Variable Regression for Multiple Discrete Outcomes
• Latent Class Models for Joint Analysis of Longitudinal Biomarker and Event Process Data
• On Profile Likelihood
• Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm
• Pattern-Mixture Models for Multivariate Incomplete Data
• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
• Informative Drop-Out in Longitudinal Data Analysis
• Modelling Progression of CD4-Lymphocyte Count and Its Relationship to Survival Time
• Joint modelling of longitudinal measurements and event time data
• Statistical models based on counting processes

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