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.
|Number of pages||18|
|Journal||Communications in Statistics: Simulation and Computation|
|Publication status||Published - 2022|
Bibliographical noteFunding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1A09916819).
© 2019 Taylor & Francis Group, LLC.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modelling and Simulation