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Research ArticleResearch

Models for Counts and Particle Size Distributions of Subvisible Particle Data

Jorge Quiroz, Elsa M. Vazquez, Jeffrey Wilson, Anita Dabbara and Jason K. Cheung
PDA Journal of Pharmaceutical Science and Technology May 2021, 75 (3) 213-229; DOI: https://doi.org/10.5731/pdajpst.2020.011510
Jorge Quiroz
1Research CMC Statistics, Merck & Co., Inc., Kenilworth, NJ;
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  • For correspondence: jorge.quiroz@merck.com
Elsa M. Vazquez
2Arizona State University, Tempe, AZ; and
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Jeffrey Wilson
2Arizona State University, Tempe, AZ; and
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Anita Dabbara
3Pharmaceutical Sciences, Merck & Co., Inc., Kenilworth, NJ
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Jason K. Cheung
3Pharmaceutical Sciences, Merck & Co., Inc., Kenilworth, NJ
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Abstract

Traditional statistical analyses of subvisible particle data are usually based on either descriptive statistics, normal-based methods, or standard Poisson models. These methods often do not adequately describe the counts or particle size distribution. They usually ignore relevant information represented in the data, such as count correlation. Therefore, any meaningful analyses of subvisible particle data require a reasonable representation of counts and particle size distribution and the correlation in the data. Such comprehensive approaches are not widely available or used when analyzing subvisible particle data. In this article, we propose the use of generalized linear mixed models to analyze the counts and the particle size distribution of subvisible particle data. These models make optimal use of the information in the data and allow flexible approaches for the analyses of a wide range of data structures. They are readily accessible to practitioners through the use of modern statistical software. These models are demonstrated with two numerical examples using two different data structures.

  • Generalized linear mixed models
  • Poisson regression with normal random effects
  • Ordinal logistic regression with normal random effects models
  • Overdispersion
  • © PDA, Inc. 2021
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PDA Journal of Pharmaceutical Science and Technology: 75 (3)
PDA Journal of Pharmaceutical Science and Technology
Vol. 75, Issue 3
May/June 2021
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Models for Counts and Particle Size Distributions of Subvisible Particle Data
Jorge Quiroz, Elsa M. Vazquez, Jeffrey Wilson, Anita Dabbara, Jason K. Cheung
PDA Journal of Pharmaceutical Science and Technology May 2021, 75 (3) 213-229; DOI: 10.5731/pdajpst.2020.011510

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Models for Counts and Particle Size Distributions of Subvisible Particle Data
Jorge Quiroz, Elsa M. Vazquez, Jeffrey Wilson, Anita Dabbara, Jason K. Cheung
PDA Journal of Pharmaceutical Science and Technology May 2021, 75 (3) 213-229; DOI: 10.5731/pdajpst.2020.011510
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Keywords

  • Generalized linear mixed models
  • Poisson regression with normal random effects
  • Ordinal logistic regression with normal random effects models
  • Overdispersion

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