Related Experiment Video
Updated: Apr 26, 2026

In Vitro Methods for Comparing Target Binding and CDC Induction Between Therapeutic Antibodies: Applications in Biosimilarity Analysis
Published on: May 4, 2017
Nonparametric tests for evaluation of biosimilarity in variability of follow-on biologics
Nan Zhang1, Jun Yang, Shein-Chung Chow
1a Amgen, Inc ., Thousand Oaks , California , USA.
Abstract:
As more biologic products are going off patent protection, the development of follow-on biologic products (also known as biosimilars) has gained much attention from both the biotechnology industry and regulatory agencies. Unlike small molecules, the development of biologic products is not only more complicated but also sensitive to a small change in procedure/environment during the manufacturing process. In practice, biologics are expected to have much larger variation, which will potentially impact the product quality and potency. Thus, it is suggested that the assessment of biosimilarity between biologic products should take variability into consideration, in addition to average biosimilarity of endpoints of interest. In this article, we propose the use of nonparametric tests for evaluation of biosimilarity in variability between the follow-on biologic product and the reference product. Extensive simulations are conducted to compare the relative performance of the proposed methods with the adapted parametric F-test in terms of correctly concluding biosimilarity in variability. Under normality assumption, the proposed nonparametric tests are found to be comparably well with the adapted F-test. However, the proposed methods are more robust when the assumption is violated.
More Related Videos
Related Concept Videos
Bioequivalence Data: Statistical Interpretation
Drug Products: Biologics, Biosimilars and Interchangeables
Bioequivalence studies: Biowaivers
Bioequivalence of Drugs: Drugs with Multiple Indications
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Bioequivalence: Overview

