Spss 16.0 for window

In order to observe the classification and assess the variation of LJF and LF, the scatter plot of all the samples including LJF and LF was made according to the amount of secologanic acid using GraphPad Prism 5 software. The rank sum test was utilized to confirm that there were differences in the amount of secologanic acid between LJF and LF (SPSS 16.0 for Windows, IBM, Armonk, NY, USA).

Statistical analyses were performed using SPSS 16.0 for Windows (IBM, Armonk, NY, USA). Quantitative data were compared between groups using Student’s t-test. Categorical data were analyzed by the χ² test or Fisher’s exact test. OS and RFS rates were calculated according to the Kaplan–Meier method, and differences were analyzed using the log-rank test. Univariate and multivariate analyses were performed using the Cox proportional hazards regression model. P<0.05 was considered statistically significant.

Differences between groups were assessed by ANOVA. In all cases, P < 0.05 was considered statistically significant. We drew a receiver operator characteristic (ROC) curve to calculate the area under curve (AUC) to discriminate CRSwNP patients from normal subjects. SPSS 16.0 for Windows (IBM, Chicago, USA) was used for ROC analyses and other statistical analyses were performed using GraphPad Prism 7.0 software (GraphPad Software, La Jolla, CA).

Statistical analyses were performed using SPSS 16.0 for Windows (IBM, Chicago, IL). After assessing the normality of the distribution of the continuous variables, they were compared either by a Student’s t-test or one-way ANOVA with LSD post hoc analysis, or using a Mann–Whitney U test or Kruskal–Wallis H test, for those with normal and non-normal distributions, respectively. The correlations between the continuous variables were assessed using a Pearson’s correlation test.
The differences in genotype distribution between the patient and control groups were assessed with χ² tests in 2 × 2 contingency tables. The odds ratios (OR) and 95% confidence intervals (CI) were obtained by applying the binary logistic regression. Outputs with p < 0.05 were considered statistically significant.

PCA, an unsupervised pattern recognition method, has been used for quality control of traditional Chinese medicine. According to the contents of 32 components, PCA was applied to classify and distinguish Ophiopogonis Radix from different habitats by Simca-P 13.0 software (Umetrics AB, Umea, Sweden). In addition, PLS-DA was performed to differentiate CMD and ZMD and find the differential constituents with variable importance in the projection (VIP) values. Besides, all detected components data were statistically analyzed by t-test (SPSS 16.0 for Windows, IBM, Armonk, NY, USA), which was used to find the differential constituents for the classification between CMD and ZMD. According to the results of quantitative determination and t-test, the boxplots were charted by Origin pro 8 (Origin Lab, Northampton, MA, USA) to obtain an overview of the metabolite distribution and analyze the discrimination of CMD and ZMD. GRA can compensate for the shortcomings in statistical regression by Grey analysis, and be applied to evaluate the samples quality by measuring the approximate correlation between sequences. GRA was carried out to evaluate the quality of CMD and ZMD based on the contents of 32 constituents.

SPSS 16.0 for Windows (IBM, Armonk, NY) was used for analysis. To minimize the impact of potential confounders and selection bias, propensity score analysis was used to compensate for the differences in baseline patient characteristics between the two groups of patients. Patients in the two groups were 1:1 matched using the nearest propensity score on the logit scale. Variables that could influence the outcomes of treatment were matched, including age, gender, body mass index (BMI), ASA status class, and maximal lesion size. After PSM, differences in continuous and categoric clinical characteristics were compared.
Continuous data are presented as mean and SD and were analyzed with two-sample Student’s t tests for independent data. Categorical variables are given as a count and percentage of patients and analyzed with the χ² or Fisher’s exact test. All tests were two-sided, P-values < 0.05 were considered statistically significant. SPSS Statistics 16.0 (IBM Corp, Armonk, NY) was used for statistical evaluations.