• Yu-Kang Tu, Francesco D'Aiuto, Vibeke Baelum, and Mark S Gilthorpe.
• Department of Periodontology, Leeds Dental Institute, Leeds, UK. y.k.tu@leeds.ac.uk
• Eur. J. Oral Sci. 2009 Aug 1;117(4):343-50.

AbstractMany studies in dental research are based on repeated measurements of several continuous variables. Statistical analyses of such data require advanced methods to explore the complexity of information within the data. Currently, the most frequently adopted approach is to undertake multiple univariate tests. Occasionally, more advanced and sophisticated statistical methodologies, such as multilevel modelling and generalized estimating equation, have been used. In the last decade, a novel statistical methodology known as latent growth curve modelling has been developed in the social sciences. Latent growth curve modelling can be considered a special application of structural equation modelling and is generally conducted using structural equation modelling software. Recent development of statistical theory shows that latent growth curve modelling is equivalent to multilevel modelling, and both approaches yield identical results. However, in some study designs latent growth curve modelling can provide a more flexible framework of statistical modelling than multilevel modelling and generalized estimating equation for longitudinal data. The aim of this article was to present a non-technical introduction to latent growth curve modelling for dental researchers. The emphasis was on conceptual understanding, rather than mathematical rigor, so path diagrams were used for visual presentations of various statistical models. When properly applied, latent growth curve modelling has great potential to give new directions for future longitudinal dental research.

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