Spss 21.0 statistics

We used SPSS 21.0 Statistics (IBM, USA) for data analysis. Normally distributed continuous data were described by mean and standard deviation. Independent t-test was used for intergroup comparison. The discrete data were described by frequency, composition ratio, or percentage, and the chi-square and Mann–Whitney U tests were used. A logistic regression model was constructed, with hypoglycemia as an independent result. Stepwise logistic regression was applied to the factors with a single-factor logistic regression P value < 0.10 to screen for hypoglycemia risk factors. Patient data were substituted into the logistic regression equation to calculate the predicted probability of hypoglycemia for each patient in the verification library. Receiver operating characteristic (ROC) curve was used to predict probability. And bootstrap method was used for internal verification. According to the predicted probability of hypoglycemia and the actual occurrence of hypoglycemia, the Hosmer–Lemeshow test was used to verify the calibration degree of the established logistic regression model.

Data were analyzed using Excel and IBM SPSS 21.0 Statistics software. The normal distribution of data was checked using the Shapiro-Wilk test. Normally distributed data were expressed as mean ± SD, while non-normally distributed data were expressed as the median (25th-75th percentiles). Differences among the 3 periods were analyzed by one-way repeated measures ANOVA and by the use of Bonferroni correction for multiple comparisons. A P value < 0.05 was considered as statistically significant.

Statistical analysis was executed using SPSS 21.0 Statistics (IBM, Armonk, NY). The median and range were used for the quantitative variables, and the Mann–Whitney U test was used to compare groups. Qualitative variables were expressed as percentages (%) or numbers(n), and differences between groups were analyzed using the Chi-squared test or Fisher's exact test. A two-tailed P value less than 0.05 was considered as an indicator of statistical significance.

All the statistical analyses were performed using IBM SPSS 21.0 Statistics (Statistical Package for Social Sciences, IBM Corp., Chicago, IL, USA) and Microsoft Excel 2016 software. To evaluate the sensitivity, specificity, positive and negative predictive values, and positive and negative probability coefficients, we chose the ELISA and CLIA assays as gold standard. Positive agreement percentage and Cohen’s kappa in all samples were collected before vaccination; at 21 days; and at 1, 2, 3, and 6 months after vaccination. With a range between 0 and 1, a kappa value of 0.40 denotes poor agreement, a value between 0.40 and 0.75 denotes fair/good agreement, and a value above 0.75 denotes excellent agreement. A value of p < 0.05 was considered statistically significant, and a 95% confidence interval (CI) was reported for each metric. The sensitivity, specificity, positive predictive value (PVP), negative predictive value (PVN), positive probability ratio (CPP), and negative probability ratio (CPN) were calculated for each serological test.
To verify the accuracy and applicability of the test in clinical practice, it must have a degree of sensitivity, specificity, general agreement, and a degree of concordance greater than 90%. Finally, the percentages of postvaccination seroconversion were compared with the three immunological trials by age group.