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Bayesian Modeling and Inference for Nonignorably Missing Longitudinal Binary Response Data with Applications to HIV Prevention Trials
Missing data are frequently encountered in longitudinal clinical trials. To better monitor and understand the progress over time, one must handle the missing data appropriately and examine whether the missing data mechanism is ignorable or nonignorable. In this article, we develop a new probit model...
Tallennettuna:
| Julkaisussa: | Stat Sin |
|---|---|
| Päätekijät: | , , , , |
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
2018
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6309964/ https://ncbi.nlm.nih.gov/pubmed/30595637 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.5705/ss.202016.0319 |
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