Statistical Considerations in Analysis of Bounded Bioanalytical Data

Music City Center 

Pharmaceutical research that involves bounded bioanalytical data often requires the calculation of statistical intervals to establish quality specifications and evaluate the integrity of pharmaceutical products. An illustrative technique is size exclusion chromatography (SEC), a widely used bioanalytical method for determining drug product purity by quantifying the proportion of monomers. To ensure the quality of these products, statistical intervals are developed to assess whether a significant proportion of production batches meets a high standard for the percentage of monomer content. Many traditional statistical interval derivations often rely on the assumption of a normal distribution. However, such assumption can lead to unreliable results and potentially inaccurate evaluations of product quality. To effectively tackle these challenges, it is crucial to employ alternative statistical methods that are tailored for bounded data. In this study, we survey and compare three inferential methods for developing statistical intervals based on the Kumaraswamy distribution. We illustrate the benefits of our proposed methods using simulations and a real data example.

Fiducial-based tolerance interval

Fiducial prediction interval

Specification

Kumaraswamy distribution 

Biopharmaceutical Section