Document Detail


Rank regression in stability analysis.
MedLine Citation:
PMID:  12921394     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Stability data are often collected to determine the shelf life of certain characteristics of a pharmaceutical product, for example, a drug's potency over time. Statistical approaches such as the linear regression models are considered as appropriate to analyze the stability data. However, most of these regression models in both theory and practice rely heavily on their underlying parametric assumptions, such as normality of the continuous characteristics or their transformations. In this article, we propose and study some rank-based regression procedures for the stability data when the linear regression models are semiparametric with unspecified error structure. Numerical studies including Monte Carlo simulations and practical example are demonstrated with the proposed procedures as well.
Authors:
Ying Qing Chen; Annpey Pong; Biao Xing
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of biopharmaceutical statistics     Volume:  13     ISSN:  1054-3406     ISO Abbreviation:  J Biopharm Stat     Publication Date:  2003 Aug 
Date Detail:
Created Date:  2003-08-18     Completed Date:  2004-03-09     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9200436     Medline TA:  J Biopharm Stat     Country:  United States    
Other Details:
Languages:  eng     Pagination:  463-79     Citation Subset:  IM    
Affiliation:
Division of Biostatistics, School of Public Health, University of California, Berkeley, California 94720, USA. yqchen@stat.berkeley.edu
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MeSH Terms
Descriptor/Qualifier:
Drug Stability*
Drug Storage* / standards,  statistics & numerical data
Linear Models*
Time Factors

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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