Affordable Access

deepdyve-link
Publisher Website

A comparative study of confidence intervals to assess biosimilarity from analytical data.

Authors
  • Quiroz, Jorge1
  • Montes, Richard2
  • Shi, Heliang2
  • Roychoudhury, Satrajit3
  • 1 Merck & Co. Inc., Research CMC Statistics Kenilworth, NJ, USA.
  • 2 Pfizer Inc., Biosimilar Pharmaceutical Sciences Lake Forest, IL, USA.
  • 3 Pfizer, Inc., Global Biometrics & Data Management Peapack, NJ, USA.
Type
Published Article
Journal
Pharmaceutical statistics
Publication Date
May 01, 2019
Volume
18
Issue
3
Pages
316–328
Identifiers
DOI: 10.1002/pst.1925
PMID: 30644636
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Assessment of analytical similarity of tier 1 quality attributes is based on a set of hypotheses that tests the mean difference of reference and test products against a margin adjusted for standard deviation of the reference product. Thus, proper assessment of the biosimilarity hypothesis requires statistical tests that account for the uncertainty associated with the estimations of the mean differences and the standard deviation of the reference product. Recently, a linear reformulation of the biosimilarity hypothesis has been proposed, which facilitates development and implementation of statistical tests. These statistical tests account for the uncertainty in the estimation process of all the unknown parameters. In this paper, we survey methods for constructing confidence intervals for testing the linearized reformulation of the biosimilarity hypothesis and also compare the performance of the methods. We discuss test procedures using confidence intervals to make possible comparison among recently developed methods as well as other previously developed methods that have not been applied for demonstrating analytical similarity. A computer simulation study was conducted to compare the performance of the methods based on the ability to maintain the test size and power, as well as computational complexity. We demonstrate the methods using two example applications. At the end, we make recommendations concerning the use of the methods. © 2019 John Wiley & Sons, Ltd.

Report this publication

Statistics

Seen <100 times