Objective: To present a new model for analyzing longitudinal data. The trilinear model is superior to the commonly used linear model that includes the flawed assumption that decline is uniform throughout the course of disease--an assumption that does not correspond to clinical observations.
Design: A longitudinal cohort sample was used to compare the linear and trilinear models. Simulated longitudinal data were generated to assess classification errors with the trilinear model.
Subjects and setting: The subjects were 80 patients with Alzheimer's disease tested in a hospital out-patient clinic.
Methods: The trilinear model describes Alzheimer's disease as proceeding through three periods: An initial period of stability before detectable decline, a period of decline, and a final period of stability during which there is no further detectable decline. A program for the Apple Macintosh computer is available at no charge to apply the model to data.
Findings: The analyses indicated that the trilinear model provides a better reflection of decline in Alzheimer's disease than does the linear model. This advantage is present whether the periods of stability reflect a "true" lack of decline or insensitivity of a measurement instrument.
Conclusions: The trilinear model provides not only a more accurate estimate of the average rate of change, but also (when possible) estimates of the point at which decline begins and ends. Also, more detailed comparisons of tests could be made by using the trilinear parameters. The trilinear model would benefit researchers engaged in longitudinal research of progressive disorders.