Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity AnalysisDrawing from the authors' own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal studies. To illustrate these methods, the authors employ |
Contents
Description of Motivating Examples | 1 |
Regression Models for Longitudinal Data | 15 |
Methods of Bayesian Inference | 39 |
Worked Examples using Complete Data | 72 |
Missing Data Mechanisms and Longitudinal Data | 85 |
Inference about FullData Parameters under Ignorability | 115 |
Case Studies Ignorable Missingness | 145 |
Models for Handling Nonignorable Missingness | 165 |
Informative Priors and Sensitivity Analysis | 216 |
Case Studies Nonignorable Missingness | 233 |
Distributions | 268 |
| 271 | |
Author Index | 292 |
| 298 | |
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Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and ... Michael J. Daniels,Joseph W. Hogan No preview available - 2008 |
Common terms and phrases
approach assume baseline Bayesian binary data Chapter components computed conditional distribution conjugate priors correlation covariance matrix covariance model covariate effects credible intervals Daniels data augmentation denote departures from MAR dependence dropout Example follow-up full conditional distribution full-data distribution full-data model full-data response model function GARP Gibbs sampler Growth Hormone hazard of dropout identified ignorability illustrate improper priors incomplete data informative priors joint distribution linear logit longitudinal data MCMC measurement missing data mechanism missing responses missingness MNAR multivariate normal distribution multivariate probit nonidentified nonignorable nonparametric normal distribution observed data likelihood p(ymis p(yobs parameterization parametric selection models pattern mixture model posterior distribution posterior mean posterior predictive prior distributions random effects model regression model schizophrenia Section sensitivity analysis sensitivity parameters smoking cessation specified Statistical structure tion treatment effect treatment group variance vector week WinBUGS Y₁ Ymis Yobs
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