Sample size/power calculations for population pharmacodynamic experiments involving repeated-count measurements

Repeated discrete outcome variables such as count measurements often arise in pharmacodynamic experiments. Count measurements can only take nonnegative integer values; this and correlation between repeated measurements from an individual make the design and analysis of repeated-count data special. Sample size/power calculation is an important part of clinical trial design to ensure adequate power for detecting significant effect, and it is often based on the procedure for analysis. This paper describes an approach for calculating sample size/power for population pharmacokinetic/pharmacodynamic experiments involving repeated-count measurements modeled as a Poisson process based on mixed-effects modeling technique. The noncentral version of the Wald chi(2) test is used for testing parameter/treatment significance. The approach was applied to two examples and the results were compared to results obtained from simulations in NONMEM. The first example involves calculating the power of a design to detect parameter significance between two groups: placebo and treatment group. The second example involves characterization of the dose-efficacy relationship of oxybutynin using a mixed-effects modeling approach. Weekly urge urinary incontinence episodes (a discrete count variable) is the primary efficacy variable and is modeled as a Poisson variable. A prospective study based on two different formulations of oxybutynin was designed using published population pharmacokinetic/pharmacodynamic model. The results of simulation studies showed good agreement between the proposed method and NONMEM simulations.