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Analysis of multiple-dose bioequivalence studies
V M Chinchilli1, J D Esinhart, W H Barr
1Center for Biostatistics and Epidemiology, College of Medicine, Penn State University, Hershey 17033.
Journal of Biopharmaceutical Statistics
|November 1, 1994
Summary
This study introduces a statistical model for analyzing pharmacokinetic data in bioequivalence trials. The model provides accurate estimators and a sample size formula for repeated measurements in crossover designs.
Area of Science:
- Pharmacokinetics
- Biostatistics
- Drug Development
Background:
- Bioequivalence studies are crucial for drug approval, assessing drug product similarity.
- Repeated pharmacokinetic measurements within crossover periods provide rich data.
- Existing models may not fully leverage this repeated-measure data at steady state.
Purpose of the Study:
- To develop a statistical model for analyzing repeated pharmacokinetic measurements in 2x2 crossover bioequivalence studies.
- To derive optimal estimators for pharmacokinetic parameters (AUC, CMAX) under steady-state conditions.
- To provide a sample size formula for efficient study design.
Main Methods:
- Developed a bivariate random effects model on the natural log scale for AUC and CMAX.
- Assumed no differential carryover effects.
- Derived uniformly minimum variance unbiased estimators (UMVUE), which are also restricted maximum likelihood (REML) estimators.
Main Results:
- The proposed bivariate random effects model effectively handles repeated pharmacokinetic measurements.
- Derived UMVUE/REML estimators for key pharmacokinetic variables.
- A sample size formula was developed for planning bioequivalence studies.
Conclusions:
- The new statistical model enhances the analysis of pharmacokinetic data in bioequivalence studies.
- The derived estimators and sample size formula contribute to more efficient and robust study designs.
- This approach can improve the precision of bioequivalence assessments.