Discussion on several statistical problems in establishing quality standards of standard decoctions

Since 2016, a number of studies have been published on standard decoctions used in Chinese medicine. However, there is little research on statistical issues related to establishing the quality standards for standard decoctions. In view of the currently established quality standard methods for standard decoctions, an improvement scheme is proposed from a statistical perspective. This review explores the requirements for dry matter yield rate data and index component transfer data for the application of two methods specified in Technical Requirements for Quality Control and Standard Establishment of Chinese Medicine Formula Granules, which include the average value plus or minus three times the standard deviation ((X) over bar +/- 3SD) or 70% to 130% of the average value ((X) over bar +/- 30%(X) over bar). The square-root arcsine transformation method is used as an approach to solve the problem of unreasonable standard ranges of standard decoctions. This review also proposes the use of merged data to establish a standard. A method to judge whether multiple sets of standard decoction data can be merged is also provided. When multiple sets of data have a similar central tendency and a similar discrete tendency, they can be merged to establish a more reliable quality standard. Assuming that the dry matter yield rate and transfer rate conform to a binomial distribution, the number of batches of prepared slices that are needed to establish the standard decoction quality standard is estimated. It is recommended that no less than 30 batches of prepared slices should be used for the establishment of standard decoction quality standards. (C) 2021 Tianjin Press of Chinese Herbal Medicines. Published by ELSEVIER B.V.

Automated summary

This study investigates the statistical issues related to establishing quality standards for standard decoctions in Chinese medicine and proposes an improvement scheme from a statistical perspective. The use of the square-root arcsine transformation method and merged data can help establish more reasonable and reliable quality standards.