Population pharmacodynamic experiments sometime involve repeated measurements of ordinal random variables at specific time points. Such longitudinal data presents a challenge during modelling due to correlation between measurements within an individual and often mixed-effects modelling approach may be used for the analysis. It is important that these studies are adequately powered by including an adequate number of subjects in order to detect a significant treatment effect. This paper describes a method for calculating sample size for repeated ordinal measurements in population pharmacodynamic experiments based on analysis by a mixed-effects modelling approach. The Wald test is used for testing the significance of treatment effects. This method is fast, simple and efficient. It can also be extended to account for differential allocation of subjects to the groups and unbalanced sampling designs between and within groups. The results obtained from two simulation studies using nonlinear mixed-effects modelling software (NONMEM) showed good agreement between the power obtained from simulation and nominal power used for sample size calculations. © 2009 Springer Science+Business Media, LLC.