Biomaterial Database

Regulatory affairs in biotechnology: optimal statistical designs for biomedical experiments. 1998

K C Carriere

Department of Mathematical Sciences, University of Alberta, Edmonton, Canada.

One of the major issues in all applications of biotechnology is how to regulate the process through which new technological information is produced. The end products of biotechnological applications are diverse (e.g., better drugs, better interventions, better fertilizers). Such applications should be properly regulated to obtain valid scientific findings in the most efficient way possible. Some statistically optimal designs are more popularly employed than others as regulatory tools in medical, pharmaceutical and clinical trials. The statistical and practical properties (strengths and weaknesses) are presented to better appreciate their optimality. Recent developments on some related issues are also reviewed.

UI	MeSH Term	Description	Entries
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000818	Animals	Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA.	Animal,Metazoa,Animalia
D001709	Biotechnology	Body of knowledge related to the use of organisms, cells or cell-derived constituents for the purpose of developing products which are technically, scientifically and clinically useful. Alteration of biologic function at the molecular level (i.e., GENETIC ENGINEERING) is a central focus; laboratory methods used include TRANSFECTION and CLONING technologies, sequence and structure analysis algorithms, computer databases, and gene and protein structure function analysis and prediction.	Biotechnologies
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model