Competing regression models for longitudinal data

Received zzz, revised zzz, accepted zzz The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a practical example involving pretest-posttest longitudinal data. In particular, we con-sider log-normal linear mixed models (LNLMM), generalized linear mixed models (GLMM) and models based on generalized estimating equations (GEE). We show how some special features of the data, like a non-constant coefficient of variation, may be handled in the three approaches and evaluate their performance with respect to the magnitude of standard errors of interpretable and comparable parameters. We also show how different diagnostic tools may be employed to identify outliers and comment on available software. We conclude by noting that the results are similar, but that GEE-based models may be preferable when the goal is to compare the marginal expected responses. Supplemental materials for this article are available on line