Knot selection strategies for semiparametric varying coefficient models applied to longitudinal cohorts with multiple dropout reasons
Dropout is a common source of missing data in longitudinal studies, and often occurs for reasons that may be related to an outcome of interest. When dropout depends on unobserved outcomes, even after conditioning on observable data, data are potentially missing not at random and dropout is therefore not ignorable. When the dropout mechanism is unspecified, semiparametric varying coefficient models can be used to account for non-ignorable dropout. This method is more robust than the parametric conditional linear approach. However, fitting these varying coefficient models requires the specification ...
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Moore, Camille Marie
Access restricted until June 30, 2014.