Nonignorable dropout models for longitudinal binary data with random effects: an application of Monte Carlo approximation through the Gibbs output

DOI10.1016/J.CSDA.2009.07.020zbMath1453.62062OpenAlexW2007461656MaRDI QIDQ961962

Doris Y. P. Leung, S. T. Boris Choy, Jennifer So-Kuen Chan, Wai-Yin Wan

Publication date: 1 April 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2009.07.020

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (1)

Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches

Uses Software

• WinBUGS
• BUGS

Cites Work

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• Modeling the Drop-Out Mechanism in Repeated-Measures Studies

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