Every statistical analysis should ideally 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 longitudinal data, 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 desire for observed and expected curves to be close to each other, or the well known equivalence between OLS and normal-based regression, do not hold as soon as data are incomplete, unless in very specific cases. Such facts challenge the statistician's intuition and great care may be needed when exploring, building, and checking a longitudinal or multivariate model for incomplete data.Professor Molenberghs is currently President of the International Biometric Society and is co-author (with Geert Verbeke) of the best-selling book "Linear Mixed Models for Longitudinal Data."
Return to Biostatistics Working Group