The study was renewed in 1996 to perform a comparative study of complex models on datasets from four large epidemiologic studies in the cardiopulmonary field. The models were compared as regards goodness of fit, ease of implementation, and interpretability. In addition, new statistical methods were developed to model phenomena which seemed poorly-fitted by existing models, including adult longitudinal bp and pulmonary function data. The overall goal was to develop tools for identifying appropriate classes of longitudinal statistical models. This has important public health implications, since longitudinal data continue to accumulate rapidly and no guidelines exist as to appropriate methods of analysis. Furthermore, it is often only through modelling of longitudinal data rather than through cross-sectional or separate two time-point analyses that underlying processes pertaining to change can be understood.