A Latent Autoregressive Model for Longitudinal Binary Data Subject to Informative Missingness

DOI10.1111/j.0006-341X.2002.00631.xzbMath1210.62138OpenAlexW2159479273WikidataQ30723842 ScholiaQ30723842MaRDI QIDQ3079001

Edward Suh, Paul S. Albert, Shaohua A. Wang, Dean A. Follmann

Publication date: 1 March 2011

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.0006-341x.2002.00631.x

zbMATH Keywords

longitudinal datanonignorable missing datainformative missingnessrepeated binary data

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Monte Carlo methods (65C05) Medical applications (general) (92C50)

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Uses Software

• GAUSS

Cites Work

• Estimation and comparison of Changes in the Presence of Informative Right Censoring: Conditional Linear Model
• Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modeling the Censoring Process
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• Informative Drop-Out in Longitudinal Data Analysis
• An Approximate Generalized Linear Model with Random Effects for Informative Missing Data
• Joint modelling of longitudinal measurements and event time data