Models for Counts and Particle Size Distributions of Subvisible Particle Data

References

1. 1.↵
   U.S. Food and Drug Administration. Guidance for Industry: Immunogenicity Assessment for Therapeutic Protein Products. FDA Web site, 2014. https://www.fda.gov/media//85017/ (accessed November 7, 2019).
2. 2.↵
   1. Das T. K.

   Protein Particulate Detection Issues in Biotherapeutics Development—Current Status. AAPS PharmSciTech 2012, 13 (2), 732–746.

   OpenUrlCrossRefPubMed
3. 3.↵
   1. Narhi L.

   Characterization and Biological Relevance of Protein Aggregates and Other Particles 100–200,000 nm in Size (Sub-Micron and Sub-Visible). CASSS Web site, 2019. https://cdn.ymaws.com/www.casss.org/resource/resmgr/hos_speaker_slides/2019_narhi_linda_slides.pdf (accessed November 7, 2019).
4. 4.↵
   1. Collett D.

   Modelling Binary Data, 2nd ed.; CRC Press, Chapman and Hall: London, 2013.
5. 5.↵
   1. Stroup W. W.

   Generalized Linear Mixed Models; CRC Press, Chapman and Hall: Boca Raton, Florida, 2013.
6. 6.↵
   1. Nath N.,
   2. McNeal E.,
   3. Obenhuber D.,
   4. Pillari B.,
   5. Shelton L.,
   6. Stevens-Riley M.,
   7. Sweeney N.

   Particulate Contaminants of Intravenous Medication and the Limits Set by USP General Chapter <788>. Pharmacopeial Forum 2004, 30 (6), 2272–2280.

   OpenUrl
7. 7.↵
   1. Corvari V.,
   2. Narhi L. O.,
   3. Spitznagel T. M.,
   4. Afonina N.,
   5. Cao S.,
   6. Cash P.,
   7. Cecchini I.,
   8. DeFelippis M. R.,
   9. Garidel P.,
   10. Herre A.,
   11. Koulov A. V.,
   12. Lubiniecki T.,
   13. Mahler H.-C.,
   14. Mangiagalli P.,
   15. Nesta D.,
   16. Perez-Ramirez B.,
   17. Polozova A.,
   18. Rossi M.,
   19. Schmidt R.,
   20. Simler R.,
   21. Singh S.,
   22. Weiskopf A.,
   23. Wuchner K.

   Subvisible (2–100 µm) Particle Analysis during Biotherapeutic Drug Product Development: Part 2, Experience with the Application of Subvisible Particle Analysis. Biologicals 2015, 43 (6), 457–473.

   OpenUrl
8. 8.↵
   1. Demeule B.,
   2. Messick S.,
   3. Shire S. J.,
   4. Liu J.

   Characterization of Particles in Protein Solutions: Reaching the Limits of Current Technologies. AAPS J. 2010, 12 (4), 708–715.

   OpenUrlPubMed
9. 9.↵
   1. Singh S. K.,
   2. Afonina N.,
   3. Awwad M.,
   4. Bechtold-Peters K.,
   5. Blue J. T.,
   6. Chou D.,
   7. Cromwell M.,
   8. Krause H.-J.,
   9. Mahler H.-C.,
   10. Meyer B. K.,
   11. Narhi L.,
   12. Nesta D. P.,
   13. Spitznagel T.

   An Industry Perspective on the Monitoring of Subvisible Particles as a Quality Attribute for Protein Therapeutics. J. Pharm. Sci. 2010, 99 (8), 3302–3321.

   OpenUrlCrossRefPubMed
10. 10.↵
    1. Burdick R.,
    2. LeBlond D.,
    3. Pfahler L.,
    4. Quiroz J.,
    5. Sidor L.,
    6. Vukovinsky K.,
    7. Zhang L.

    Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry; Springer International Publishing AG, 2017.
11. 11.↵
    1. Agresti A.

    Categorical Data Analysis, 3rd ed.; John Wiley & Sons, 2013.
12. 12.↵
    1. Ramirez J. G.

    Modeling Sub-Visible Particle Data: Product Held at Accelerated Stability Conditions. Presented at the 36th Midwest Biopharmaceutical Statistics Workshop (MBSW), Muncie, IN, May 20–22, 2013.
13. 13.↵
    1. Zhang H.,
    2. Lu N.,
    3. Feng C.,
    4. Thurston S. W.,
    5. Xia Y.,
    6. Zhu L.,
    7. Tu X. M.

    On Fitting Generalized Liner Mixed-Effects Models for Binary Responses Using Different Statistical Packages. Stat. Med. 2011, 30 (20), 2562–2572.

    OpenUrlCrossRefPubMed
14. 14.↵
    1. Larsen K.,
    2. Petersen J. H.,
    3. Budtz-Jørgensen E.,
    4. Endahl L.

    Interpreting Parameters in the Logistic Regression Model with Random Effects. Biometrics 2000, 56 (3), 909–914.

    OpenUrlCrossRefPubMedWeb of Science
15. 15.↵
    1. Aitchison J.,
    2. Ho C. H.

    The Multivariate Poisson-Log Normal Distribution. Biometrika 1989, 76 (4), 643–653.

    OpenUrlCrossRefWeb of Science
16. 16.↵
    1. Cameron A. C.,
    2. Trivedi K.

    Regression Analysis of Count Data, 2nd ed.; Cambridge University Press: New York, 1998.
17. 17.↵
    1. Hedeker D.

    A Mixed-Effect Multinomial Logistic Regression Model. Stat. Med. 2003, 22 (9), 1433–1446.

    OpenUrlCrossRefPubMedWeb of Science
18. 18.↵
    1. Bolker B. M.

    Generalized Linear Mixed Models: A Practical Guide for Ecology and Evolution. Trends Ecol. Evol. 2009, 24 (3), 127–135.

    OpenUrlCrossRefPubMedWeb of Science
19. 19.↵
    1. Heiny E. L.,
    2. Frisby C. C.

    An Ordinal Logistic Regression Model for the Masters Golf Tournament. Chance 2018, 31 (3), 44–58.

    OpenUrl
20. 20.↵
    SAS Institute Inc. SAS/STAT User's Guide; SAS Institute Inc: Cary, NC, 2018.
21. 21.↵
    R Core Team. R: A Language and Environment for Statistical Computing, 2018. R Foundation for Statistical Computing Web site. http://www.R-project.org (accessed November 7, 2019).
22. 22.↵
    1. Krishnamoorthy K.,
    2. Xia Y.,
    3. Xie F.

    A Simple Approximate Procedure for Constructing Binomial and Poisson Tolerance Intervals. Commun. Stat. Theory Methods 2011, 40 (12), 2243–2258.

    OpenUrl
23. 23.↵
    1. Bates D.,
    2. Machler M.,
    3. Bolker B.,
    4. Walker S.

    Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 2015, 67 (1), 1–48.

    OpenUrlCrossRefPubMed
24. 24.↵
    1. Christensen R. H. B.

    Ordinal—Regression Model for Ordinal Data. R Package Version 2019.3-9, 2019. http://www.cran.r-project.org/package=ordinal/ (accessed November 7, 2019).
25. 25.↵
    1. Young D. S.

    An R Package for Estimating Tolerance Intervals. J. Stat. Softw. 2010, 36 (5), 1–39.

    OpenUrlCrossRefPubMed