A reanalysis of a longitudinal scleroderma clinical trial using non-ignorable missingness models
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Publication:2455407
DOI10.1016/j.jspi.2007.04.005zbMath1124.62072OpenAlexW2023305997MaRDI QIDQ2455407
Weng Kee Wong, W. John Boscardin, Xiaohong Yan
Publication date: 24 October 2007
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jspi.2007.04.005
Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)
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Cites Work
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- Random-Effects Models for Longitudinal Data
- Inference from iterative simulation using multiple sequences
- Monte Carlo methods in Bayesian computation
- Bayesian model comparison: log scores and \(DIC\)
- Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable
- Diagnostics for Joint Longitudinal and Dropout Time Modeling
- Modeling Longitudinal Data with Nonignorable Dropouts Using a Latent Dropout Class Model
- Latent Pattern Mixture Models for Informative Intermittent Missing Data in Longitudinal Studies
- Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modeling the Censoring Process
- Estimation in Covariance Components Models
- A Predictive Approach to Model Selection
- Analysis of Longitudinal Data with Non-Ignorable Non-Monotone Missing Values
- A Random Effects Transition Model For Longitudinal Binary Data With Informative Missingness
- Sensitivity Analysis of Continuous Incomplete Longitudinal Outcomes
- A Random Pattern-Mixture Model for Longitudinal Data With Dropouts
- Mixed Effects Logistic Regression Models for Longitudinal Binary Response Data with Informative Drop-Out
- Reparameterizing the Pattern Mixture Model for Sensitivity Analyses Under Informative Dropout
- Bayesian Measures of Model Complexity and Fit
- Pattern-Mixture Models for Multivariate Incomplete Data
- Informative Drop-Out in Longitudinal Data Analysis
- Modelling Progression of CD4-Lymphocyte Count and Its Relationship to Survival Time
- An Approximate Generalized Linear Model with Random Effects for Informative Missing Data
- Modeling the Drop-Out Mechanism in Repeated-Measures Studies
- Strategies to fit pattern-mixture models
- Incorporating prior beliefs about selection bias into the analysis of randomized trials with missing outcomes
- Linear mixed models for longitudinal data
- Influence analysis to assess sensitivity of the dropout process.
- Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)
