
doi: 10.1002/sim.5564
pmid: 22903263
To develop a biosimilar product, it is essential to demonstrate the biosimilarity between the proposed biosimilar product and the reference product first in terms of quality in a stepwise approach that can then help inform the extent of safety and efficacy data that will be required to establish biosimilarity. These comparability studies should have direct side‐by‐side comparisons of the test and the reference products. In this paper, we develop a statistical method for unpaired head‐to‐head quality attribute comparisons. The method uses a plausibility interval derived from comparing the reference against the reference itself as the goalpost for claiming comparability. The idea behind this is that any observed difference between the reference and the reference itself should be considered as the random noise and as a part of the variability. We illustrate the performance of the proposed method by using simulation and real data sets. Copyright © 2012 John Wiley & Sons, Ltd.
biosimilarity, quality attributes, assay variability, tolerance interval, United States, Applications of statistics to biology and medical sciences; meta analysis, process variability, plausibility interval, Drug Evaluation, biosimilars, bioprocess changes, comparability, Biosimilar Pharmaceuticals, Algorithms
biosimilarity, quality attributes, assay variability, tolerance interval, United States, Applications of statistics to biology and medical sciences; meta analysis, process variability, plausibility interval, Drug Evaluation, biosimilars, bioprocess changes, comparability, Biosimilar Pharmaceuticals, Algorithms
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 17 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
