RT Journal Article
SR Electronic
T1 Models for Counts and Particle Size Distributions of Sub-visible Particle Data
JF PDA Journal of Pharmaceutical Science and Technology
JO PDA J Pharm Sci Technol
FD Parenteral Drug Association (PDA)
SP pdajpst.2020.011510
DO 10.5731/pdajpst.2020.011510
A1 Jorge Quiroz
A1 Elsa M. Vazquez
A1 Jeffrey Wilson
A1 Anita Dabbara
A1 Jason Cheung
YR 2020
UL http://journal.pda.org/content/early/2020/11/16/pdajpst.2020.011510.abstract
AB Traditional statistical analyses of sub-visible 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, or ignore relevant information represented in the data, such as count correlation. Therefore, any meaningful analyses of sub-visible particle data require a reasonable representation of counts and the particle size distribution as well as a means of addressing the correlation in the data. Such comprehensive approaches are not widely available or known when analyzing sub-visible particle data. In this paper, we propose the use of generalized linear mixed models to analyze counts and the particle size distribution of sub-visible 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.