A marginalized conditional linear model for longitudinal binary data when informative dropout occurs in continuous time

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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 covariate effects in CLMs are not readily available if nonidentity links are used in the mean struct...

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ژورنال

عنوان ژورنال: Biostatistics

سال: 2011

ISSN: 1468-4357,1465-4644

DOI: 10.1093/biostatistics/kxr041