Effects of between-batch variability on the type I error rate in biosimilar development

Junhui Park, Seung Ho Kang

Research output: Contribution to journalArticle

Abstract

Biological products are known to have some between-batch variation. However, the traditional method to assess biosimilarity does not consider such between-batch variation. Beta-binomial models and linear random effect models are considered in order to incorporate between-batch variation for the binary endpoints and the continuous endpoints, respectively. In this article, emphasis is on the beta-binomial models for the binary endpoint case. For the linear random effect models of the continuous endpoint case, we cite relevant references along with conducting some simulation studies. Overall, we show that the type I error rates are inflated when biosimilarity is evaluated by the traditional method, which ignores between-batch variation.

Original languageEnglish
JournalCommunications in Statistics: Simulation and Computation
DOIs
Publication statusAccepted/In press - 2019 Jan 1

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Type I Error Rate
Batch
Beta-binomial Model
Random Effects Model
Linear Model
Binary
Simulation Study
Statistical Models

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation

Cite this

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