Related Experiment Video
Updated: Aug 17, 2026

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
Published on: January 31, 2014
ANCOVA approach for shelf life analysis of stability study of multiple factor designs
Yi Tsong1, Wen-Jen Chen, Chi Wan Chen
1Office of Biostatistics, Center for Drug Evaluation and Research, FDA, Rockville, Maryland 20857, USA. tsong@cder.fda.gov
Abstract:
For a traditional multiple batch stability design with no other factor, the conventional analysis is analysis of covariance (ANCOVA) modeling using F-tests based on type I sum of squares to determine whether the batches may be pooled for a common estimate of the linear regression line(s). In the last decade, many multiple factor designs were proposed in stability studies. With the objective of model selection, the generalization of the conventional ANCOVA model using type I sum of squares to designs with multiple factors requires a prespecified hierarchical pooling test ordering to determine whether any of the factors may be eliminated. Different shelf life estimates may be derived using different hierarchical pooling test orderings. On the other hand, setting the hierarchical ordering can be subjective and controversial. The stepwise modeling based on F-tests using type III sum of squares for model determination and factor elimination is proposed to eliminate such difficulties.
More Related Videos
08:27Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
Published on: September 27, 2019
13:54A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
Published on: August 18, 2023
Related Concept Videos
Factorial Design
What is an ANOVA?
Before performing ANOVA, one must ensure that the samples used for this analysis have three crucial characteristics or statistical assumptions. The first assumption states that the samples should be drawn from normally distributed samples, while the second requires that all the drawn samples should be randomly and...
One-Way ANOVA
Two-Way ANOVA
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Methods of Medium Optimization