Mixed-effects regression models have become the standard tools of analyzing data from longitudinal studies. This talk first describes mixed-effects models and then examines how the choice of age and time in modeling observational longitudinal data can affect the results. In particular, age can be decomposed into two components: the age at entry into the study (first age) and the longitudinal follow-up time. The implication of using age or first age and time is described for a number of possible linear mixed-effects models that may be used to describe the longitudinal data. The two approaches are illustrated using a number of different examples of data taken from the Baltimore Longitudinal Study of Aging (BLSA). The examples illustrate that the added flexibility provided by the first age and time approach is usually necessary to adequately describe the data.