JSM 2005 - Toronto

Abstract #302848

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 255
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #302848
Title: Formal and Informal Mixed Model Selection When Data Are Incomplete
Author(s): Geert Molenberghs*+
Companies: Limburgs Universitair Centrum
Address: Universitaire Campus D, Diepenbeek, B3590, Belgium
Keywords: longitudinal data ; missing data ; mixed model ; model selection ; graphical display
Abstract:

Every statistical analysis ideally should start with data exploration, proceed with a model selection strategy, and assess goodness of fit. Graphical tools are an important component of such a strategy. In the context of mixed models, these steps are not necessarily straightforward, and the issues are compounded when data are incomplete. Indeed, some familiar results from complete (balanced) data, such as the desirability for observed and expected curves to be close to each other or the well known similarity between OLS and normal-based regression, do not hold as soon as data are incomplete, except in restrictive cases. Such facts challenge the statistician's intuition, so great care may be needed when exploring, building, and checking a (mixed) model for incomplete longitudinal data.

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