Statistical Evaluation of Clinical Trial Design for a Population Pharmacokinetic Study —A Case Study—

Abstract

  A population pharmacokinetic substudy design of a new chemical entity was evaluated based on the bias in parameter estimates and the power of detecting a specific subpopulation showing different clearance using a clinical trial simulation approach. The effect of analysis algorithms on type I error was also assessed. The design factors included the number of patients (n=100-300) and the number of sampling points per patient (n=2-6). Simulation data were generated from a model developed based on a Phase I study. The power was evaluated for a percentile of test statistics obtained by the simulation study. The clearance (CL) related parameters were estimated with sufficient accuracy in all study designs and all analysis algorithms: the first order (FO), first order conditional estimation (FOCE) and first order conditional estimation with interaction (FOCE-INTER) methods. With the FO and FOCE methods, the type I error rate increased as the frequency of sampling from each patient became higher, but such increase was hardly observed with the FOCE-INTER method. The power tended to depend on the size of the subpopulation. A large difference was found in the power of detecting a specific subpopulation showing a clearance decrease of 30% or 50%. Therefore, the most dominant factors controlling power would be the size of the subpopulation and the decreasing ratio of CL in the subpopulation. These findings obtained by the clinical trial simulation approach are useful for optimization of study design and determination of the limits of evaluation.