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Statistical Modeling of Longitudinal Data with Non-ignorable Non-monotone Missingness with Semiparametric Bayesian

Yu Cao1, Nitai D Mukhopadhyay1

  • 1Department of Biostatistics, Virginia Commonwealth University, 830 East Main Street, Richmond, VA.

Sankhya. Series B (2008)
|June 21, 2021
PubMed
Summary

This study introduces a novel statistical approach using latent class analysis and pattern-mixture models to handle missing outcome data in longitudinal studies, improving accuracy for small sample sizes.

Keywords:
Bayesian nonparametric analysisMissing dataNon-ignorable missingnessNon-monotone missingnessimputation

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