Subjective evaluations are nowadays applied more commonly in cosmetic product assessment. They are used in quality control, product development steps and efficacy studies for claim support. Several studies have been p...Subjective evaluations are nowadays applied more commonly in cosmetic product assessment. They are used in quality control, product development steps and efficacy studies for claim support. Several studies have been published to determine the adequate number of panelists, but recommendations and guidelines dealing with this topic are rare in the cosmetic sector. The aim of the present pilot study was to recommend a suitable study plan and define the adequate consumer panel size for cosmetic consumer assessment. A questionnaire-based product evaluation study, with three different cosmetic products, was organized as a consumer test using a seven-point scale. As a last step, a specific statistical calculation was performed to define the minimum sample size. It showed that the minimum sample size, besides the obvious statistical parameters of standard deviation and confidence interval, also depends on age and gender of the panelists and product assessment item. Utilizing a CI of 95% a minimum of 60 panelists seems to be sufficient for home-use-test (HUT) with a given seven-point scale. A minimum of 101 panelists are shown to be sufficient utilizing a CI of 99%.展开更多
Diversity analysis and taxonomic profiles can be generated from marker-gene sequence data with the help of many available computational tools.The Quantitative Insights into Microbial Ecology Version 2(QIIME2)has been ...Diversity analysis and taxonomic profiles can be generated from marker-gene sequence data with the help of many available computational tools.The Quantitative Insights into Microbial Ecology Version 2(QIIME2)has been widely used for 16S rRNA data analysis.While many articles have demonstrated the use of QIIME2 with suitable datasets,the application to preclinical data has rarely been talked about.The issues involved in the pre-clinical data include the low-quality score and small sample size that should be addressed properly during analysis.In addition,there are few articles that discuss the detailed statistical methods behind those alpha and beta diversity significance tests that researchers are eager to find.Running the program without knowing the logic behind it is extremely risky.In this article,we first provide a guideline for analyzing 16S rRNA data using QIIME2.Then we will talk about issues in pre-clinical data,and how they could impact the outcome.Finally,we provide brief explanations of statistical methods such as group significance tests and sample size calculation.展开更多
文摘Subjective evaluations are nowadays applied more commonly in cosmetic product assessment. They are used in quality control, product development steps and efficacy studies for claim support. Several studies have been published to determine the adequate number of panelists, but recommendations and guidelines dealing with this topic are rare in the cosmetic sector. The aim of the present pilot study was to recommend a suitable study plan and define the adequate consumer panel size for cosmetic consumer assessment. A questionnaire-based product evaluation study, with three different cosmetic products, was organized as a consumer test using a seven-point scale. As a last step, a specific statistical calculation was performed to define the minimum sample size. It showed that the minimum sample size, besides the obvious statistical parameters of standard deviation and confidence interval, also depends on age and gender of the panelists and product assessment item. Utilizing a CI of 95% a minimum of 60 panelists seems to be sufficient for home-use-test (HUT) with a given seven-point scale. A minimum of 101 panelists are shown to be sufficient utilizing a CI of 99%.
基金S.N.Rai was partly supported with Wendell Cherry Chair in Clinical Trial Research Fund and NIH grants 5P20GM113226(CJM),1P42ES023716(PI:Sanjay Srivastava)and 1P20GM125504(PI:Richard Lamont).C.Qian was supported by the National Institutes of Health grant 5P50AA024337(CJM)and the University of Louisville Fellowship.
文摘Diversity analysis and taxonomic profiles can be generated from marker-gene sequence data with the help of many available computational tools.The Quantitative Insights into Microbial Ecology Version 2(QIIME2)has been widely used for 16S rRNA data analysis.While many articles have demonstrated the use of QIIME2 with suitable datasets,the application to preclinical data has rarely been talked about.The issues involved in the pre-clinical data include the low-quality score and small sample size that should be addressed properly during analysis.In addition,there are few articles that discuss the detailed statistical methods behind those alpha and beta diversity significance tests that researchers are eager to find.Running the program without knowing the logic behind it is extremely risky.In this article,we first provide a guideline for analyzing 16S rRNA data using QIIME2.Then we will talk about issues in pre-clinical data,and how they could impact the outcome.Finally,we provide brief explanations of statistical methods such as group significance tests and sample size calculation.
