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Biostatistics Advance Access published online on March 23, 2007
Biostatistics, doi:10.1093/biostatistics/kxm004
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© The Author 2007. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

The effect of miss-specified baseline characteristics on inference for longitudinal trends in linear mixed models

Geert Verbeke* and Steffen Fieuws

Biostatistical Centre, Katholieke Universiteit Leuven, U.Z. St.-Rafaël. Kapucijnenvoer 35, B-3000 Leuven, Belgium geert.verbeke{at}med.kuleuven.be

* To whom correspondence should be addressed.

The main advantage of longitudinal studies is that they can distinguish changes over time within individuals (longitudinal effects) from differences among subjects at the start of the study (baseline characteristics, cross-sectional effects). Often, especially in observational studies, longitudinal trends are studied after correction for many potentially important baseline differences between subjects. We show that, in the context of linear mixed models, inference for longitudinal trends is in general biased if a wrong model for the baseline characteristics is used. However, we will argue that this bias is small in most practical situations and completely vanishes in the special case of a growth curve model for complete balanced data. In the latter case, inference for longitudinal trends is completely independent of additional baseline covariates that might have been omitted from the model.

Keywords: Baseline characteristics; Growth curve model; Linear mixed model; Longitudinal data; Longitudinal trends

Received August 2, 2006; revised November 7, 2006; accepted for publication February 7, 2007.


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