In this paper we analyze serial CD4 T-cell measurements from the Los Angeles portion of the Multicenter AIDS Cohort Study. Our emphasis is on developing a plausible and parsimonious model to describe the stochastic process underlying the patterns of CD4 measurements. The stochastic process that we use enables us to investigate the concept of derivative tracking, for which it is assumed that the rank order of the individual's slopes is maintained over time. A general model for the analysis of longitudinal repeated measures data iswhereYi(tij) is the measurement of subjectiat timetij, X(tij)α represents fixed effect terms,Z(tij)birepresents random effect terms,Wi(tij) is a stochastic process allowing correlation between measurements, and εijis measurement error. In the simplest case,X(tij) andZ(tij) contain the times of measurements. ForWi(tij), we use a two-parameter integrated Ornstein-Uhlenbeck (OU) process. The OU process is the continuous mean zero Gaussian Markov process, which includes Brownian motion and white noise as special limiting cases. This model is a continuous-time version of an AR(1) process for the deviations of the derivative ofyfrom the expected derivative ofywith respect tot.This approach is flexible and tractable as the covariance structure has a closed-form expression. The model allows unequally spaced observations and can be generalized to multivariate responses. This model enables one to assess whether individuals maintain their trajectories; that is, whether their slope ofYtracks. We find no evidence in the data that the slopes of the CD4 values track.