ABSTRACT
The purpose of this paper was to introduce the CMH χ2 test and SAS software implementation of the three kinds of R×C contingency table data. The first type was called “two-way unordered R×C contingency table data”. The CMH χ2 test corresponding to this type of data was essentially the Pearson’s χ2 test. The second type was called “R×C contingency table data with an ordinal outcome variable”. The CMH χ2 test corresponding to this kind of data was essentially a rank sum test. The third type was called “R×C contingency table data which was of two ordinal variables with different attributes”. The CMH χ2 test corresponding to the data was essentially Pearson’s correlation analysis or Spearman’s rank correlation analysis. When there were 1 or 2 “ordinal variables” in the R×C contingency table data， it was necessary to “assign or score” the ordinal variables before performing statistical analysis. In the FREQ procedure of SAS/STAT， there were four scoring methods. With different scoring approach， both the expression form and the calculation results of CMH χ2 test statistics could change accordingly.
ABSTRACT
Objective: To identify the key process parameters of the Chinese materia medica (CMM) production process by using grey relation analysis (GRA) method. Methods: Taking Lonicerae Japonicae Flos and Artemisiae Annuae Herba extraction section of Reduning Injection as an example, GRA was adopted to calculate and compare the influence of the process parameters on the quality index. Meanwhile, analytic hierarchy process (AHP) combined with Spearman rank correlation analysis was used to validate mutually. Results: According to GRA Results:, the relative importance of process parameters was ranked as follows: average volume flow rate of extraction (X4) > pH after acid adjustment (X2) > the paste temperature of extracting concentration (X7) > alcohol precipitation concentrated extract weight (X1) > hydrochloric acid weight (X3) > extraction time (X6) > relative standard deviation of flow rate (X5). The correlation coefficient between the order by GRA and that by AHP was 0.893. According to the importance of process parameters, the average volume flow rate of extraction, the pH after acid adjustment, and the paste temperature of extracting concentration were identified as the key process parameters. Conclusion: The Results: obtained in this study show the feasibility of GRA in selecting key process parameters, which can provide theoretical reference for the establishment of prediction model as well as online feedback regulation.