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A dynamic approach for reconstructing missing longitudinal data using the linear increments model
Missing observations are commonplace in longitudinal data. We discuss how to model and analyze such data in a dynamic framework, that is, taking into consideration the time structure of the process and the influence of the past on the present and future responses. An autoregressive model is used as...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2010
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3293429/ https://www.ncbi.nlm.nih.gov/pubmed/20388914 http://dx.doi.org/10.1093/biostatistics/kxq014 |