RT Journal Article
SR Electronic
T1 Models for Counts and Particle Size Distributions of Subvisible Particle Data
JF PDA Journal of Pharmaceutical Science and Technology
JO PDA J Pharm Sci Technol
FD Parenteral Drug Association (PDA)
SP 213
OP 229
DO 10.5731/pdajpst.2020.011510
VO 75
IS 3
A1 Jorge Quiroz
A1 Elsa M. Vazquez
A1 Jeffrey Wilson
A1 Anita Dabbara
A1 Jason K. Cheung
YR 2021
UL http://journal.pda.org/content/75/3/213.abstract
AB 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.