Cited by 254Optimal Experimental Design MethodsStatistical Methods in Clinical TrialsStatistical Methods and Applications

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Design of Experiments: Concept and Applications of Plackett Burman Design

Vanaja, K., Shobha Rani, R.H.·2007·Clinical Research and Regulatory Affairs

Abstract

Application of statistics has broadened far beyond its origin to various areas of research and one among them is the design of experiments. Design of experiments gathers the maximum amount of information in the lowest number of analyses. In order to optimize a process and maintain repeatability, screening designs are performed. A two-level, mathematically derived multiples of four screening design, popularly known as Plackett-Burman screening design, is thoroughly reviewed in this paper. Application of this design in various areas of research such as formulation development and analytical development has also been focused to explore its potential in screening unbiased estimates of all main effects in the smallest design.

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Metadata

Authors
Vanaja, K., & Shobha Rani, R. H.
Affiliations
Al-Ameen College of Pharmacy (IN)
Venue
Clinical Research and Regulatory Affairs
Published
2007-01
Volume
24
Issue
1
Pages
1-23
ISSN
  • 1060-1333 (ISSN-L)
  • 1532-2521
OpenAlex
W1980086200
Cited by
254
References
0
Citations by year
2026: 19 · 2025: 35 · 2024: 42 · 2023: 35 · 2022: 30 · 2021: 22 · 2020: 23 · 2019: 16 · 2018: 9 · 2017: 6 · 2016: 1 · 2015: 5 · 2013: 3 · 2012: 2
FWCI
1.44
Type
journal article
Language
en
Open access
closed
Locations
Clinical Research and Regulatory Affairs (publishedVersion)
Funders
  • U.S. Department of Energy
  • Indian Council of Medical Research
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords
Plackett-Burman design, screening designs, two-level factorial designs, main effects estimation, process optimization, formulation development, analytical method development, experimental design

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