TY - JOUR

T1 - Determination of the confidence interval of the relative standard deviation using convolution

AU - Gao, Yijie

AU - Ierapetritou, Marianthi G.

AU - Muzzio, Fernando J.

N1 - Funding Information:
Acknowledgments This work is supported by the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems, through grant NSF-ECC 0540855 and by grant NSF-0504497.

PY - 2013/6

Y1 - 2013/6

N2 - Coefficient of variation is a widely used measure of dispersion and is important in comparing variables with different units or average values. In pharmaceutical industry, it is termed as the relative standard deviation (RSD) and is used widely to describe blend concentration variability, finished dose variability, dissolution q point variability, etc. Although theoretical formula and simulation methods for the estimation of the RSD confidence interval have been developed in previous literature, they are not well known, and they are either too complex to apply easily or require intensive computation. As a result, the statistical reliability of RSD estimates are rarely evaluated, which increases process risk as well as consumer risk. In this paper, we introduce a novel convolution numerical method for the quick and straightforward estimation of RSD confidence intervals. A standard statistical distribution group is developed, denoted as the Chi-on-Mu-square distribution, which is similar to the widely used Chi-square distribution. Results indicate the Chi-square distribution itself can be a good approximation in the RSD confidence interval calculation, especially when small RSD is expected or large number of samples is involved. The effect of deviations from the normal distribution populations is also discussed.

AB - Coefficient of variation is a widely used measure of dispersion and is important in comparing variables with different units or average values. In pharmaceutical industry, it is termed as the relative standard deviation (RSD) and is used widely to describe blend concentration variability, finished dose variability, dissolution q point variability, etc. Although theoretical formula and simulation methods for the estimation of the RSD confidence interval have been developed in previous literature, they are not well known, and they are either too complex to apply easily or require intensive computation. As a result, the statistical reliability of RSD estimates are rarely evaluated, which increases process risk as well as consumer risk. In this paper, we introduce a novel convolution numerical method for the quick and straightforward estimation of RSD confidence intervals. A standard statistical distribution group is developed, denoted as the Chi-on-Mu-square distribution, which is similar to the widely used Chi-square distribution. Results indicate the Chi-square distribution itself can be a good approximation in the RSD confidence interval calculation, especially when small RSD is expected or large number of samples is involved. The effect of deviations from the normal distribution populations is also discussed.

KW - Coefficient of variation

KW - Confidence interval

KW - Quality by design

KW - Relative standard deviation

UR - http://www.scopus.com/inward/record.url?scp=84878330544&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84878330544&partnerID=8YFLogxK

U2 - 10.1007/s12247-012-9144-8

DO - 10.1007/s12247-012-9144-8

M3 - Article

AN - SCOPUS:84878330544

VL - 8

SP - 72

EP - 82

JO - AAPS PharmSciTech

JF - AAPS PharmSciTech

SN - 1530-9932

IS - 2

ER -