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A marginalized conditional linear model for longitudinal binary data when informative dropout occurs in continuous time
Within the pattern-mixture modeling framework for informative dropout, conditional linear models (CLMs) are a useful approach to deal with dropout that can occur at any point in continuous time (not just at observation times). However, in contrast with selection models, inferences about marginal cov...
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Oxford University Press
2012
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3297830/ https://www.ncbi.nlm.nih.gov/pubmed/22133756 http://dx.doi.org/10.1093/biostatistics/kxr041 |