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A Bayesian model for time-to-event data with informative censoring
Randomized trials with dropouts or censored data and discrete time-to-event type outcomes are frequently analyzed using the Kaplan–Meier or product limit (PL) estimation method. However, the PL method assumes that the censoring mechanism is noninformative and when this assumption is violated, the in...
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| 主要な著者: | , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Oxford University Press
2012
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3297827/ https://ncbi.nlm.nih.gov/pubmed/22223746 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/biostatistics/kxr048 |
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