Repeated measures analysis is a common analysis plan for clinical trials comparing change over time in quantitative trait outcomes in treatment versus control. Mixed model repeated measures (MMRM) assuming an unstructured covariance of repeated measures is a common default analysis plan, but the number of covariance parameters in this model increase quadratically as the number of repeat measures increase, leading to model convergence issues and the need for contingency analysis plans. We here describe a covariance structure that has the parsimonious features of the mixed effects model with random slopes and intercepts, but without restricting the repeated measure means to be linear with time. We demonstrate with data from completed trials that this covariance structure is typical of Alzheimer's disease. We further demonstrate that the compound symmetric model is anticonservative, and the autoregressive model is poorly powered for longitudinal trajectories that spread apart over time. Finally, we derive power calculation formulas for the chronic progressive repeated measures model that are independent of the design of the pilot studies informing the power calculations.