We applied a two-stage random effects model to pulmonary function data from 31 sarcoidosis patients to illustrate its usefulness in analysing unbalanced longitudinal data. For the first stage, repeated measurements of percentage of predicted forced vital capacity (FVC%) from an individual were modelled as a function of time since initial clinical assessment. At the second stage, parameters of this function were modelled as a function of certain patient characteristics. We used three methods for estimating the model parameters: maximum likelihood; empirical Bayes; and a two-step least-squares procedure. Similar results were obtained from each, but we recommend the empirical Bayes, since it provides unbiased estimates of variance components. Results indicated that deterioration in FVC% is associated with a higher initial FVC% value and large numbers of both total cells and eosinophils in bronchoalveolar lavage at the initial assessment. Improvement is associated with higher values of pulmonary Gallium uptake at initial assessment and race. Blacks are more likely to improve than whites.