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Correction of Bias from Non-random Missing Longitudinal Data Using Auxiliary Information
Missing data are common in longitudinal studies due to drop-out, loss to follow-up, and death. Likelihood-based mixed effects models for longitudinal data give valid estimates when the data are ignorably missing; that is, the parameters for the missing data process are distinct from those of the mai...
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| Main Authors: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2010
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4162134/ https://ncbi.nlm.nih.gov/pubmed/20029935 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/sim.3821 |
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