K.Ogungbenro and L. Aarons
Centre for Applied Pharmacokinetics Research, School of Pharmacy and Pharmaceutical Sciences University of Manchester, Manchester, United Kingdom
An important consideration in applied science studies such as clinical trials is the number of subjects to be included in the study. Since sample size is often proportional to the cost and power of the study, attempts are always made to include the minimum number of subjects in the study to balance the two effects. The Population pharmacokinetic approach has been widely used in drug development and the accuracy and the precision with which the parameters are estimated have been shown to depend on a number of design factors, including the sample size. To the best of our knowledge no analytical method has been applied to calculate the number of subjects required for these kind of studies. The use of likelihood ratio tests (with simulation) has been used to determine the number of subjects required for pharmacokinetic studies designed to detect the difference(s) in parameter(s) between two groups(1).
This study focused on the use of a confidence interval (by simulation) approach to determine sample size for pharmacokinetic studies that are not necessarily designed to detect the difference between two groups. Much has been said about over-reliance on hypothesis testing in reporting experimental results and this is partly due to the availability of sample size methods for such studies. Beal (1989) and Grieve (1991) proposed a method based on confidence intervals for calculating sample size for experiments. The proposed method for pharmacokinetic studies involves using simulation to estimate the power of a particular design by estimating the confidence interval around a parameter of choice in the model to a particular level of precision. The method was applied to a one compartment first order absorption model and operating characteristics curves were generated for the different levels of precision for the clearance parameter.
References:
1. P.I.D. Lee, Design and Power of a population Pharmacokinetics Study, Pharmaceutical Research, 18: 75-82 (2001).
2. S.L. Beal, Sample Size Determination for Confidence Interval on the population Mean and on the Difference Between Two Population Means, Biometrics, 45: 969-977 (1989).
3. A. P. Grieve, Reader Reaction, Confidence Interval and Sample Sizes, Biometrics, 47:1597-1603 (1991).
Reference: PAGE 12 (2003) Abstr 404 [www.page-meeting.org/?abstract=404]
Poster: poster