All statistical analyses were performed using the Statistical Package for Social Sciences (SPSS) version 19.0J for Windows (SPSS Inc., Chicago, Illinois, USA). Independent continuous variables were compared by the Mann–Whitney U test or Kruskal–Wallis test, and categorical variables were compared by the χ2 (Chi-square) test or Fisher’s exact test. Long-term outcomes were analyzed using Kaplan–Meier methods with the log-rank test and Cox regression. Univariate analyses were performed for all potentially confounding variables and effect modifiers. Considering the relatively small sample size, all variables with a significant level of p < 0.05 in the univariate analysis were included as independent variables.
Spss version 19.0j for windows
Manufactured by IBM
Sourced in United States
SPSS version 19.0J for Windows is a statistical software package. It provides data analysis and statistical modeling capabilities for researchers and analysts. The software allows users to import, analyze, and present data. SPSS 19.0J is designed to run on the Windows operating system.
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Lab products found in correlation
5 protocols using spss version 19.0j for windows
1
Statistical Analysis of Outcomes
2
Survival Analysis of Cancer Patients
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Cumulative rates of overall survival (OS) were analyzed by the Kaplan‐Meier method. Prognostic factors involved in OS were evaluated using the log‐rank test. In multivariate analysis, variables associated with OS were identified using stepwise Cox proportional hazards models. Variables identified using simple Cox proportional hazards models were selected for potential association with survival based on our clinical experience. Variables with significance of P < 0.05 in the simple Cox proportional hazards models were included in multifactorial Cox proportional hazard models. In multiple Cox hazards models, P < 0.05 was considered significant. All statistical analyses were carried out using Statistical Package for the Social Sciences (SPSS) version 19.0J for Windows (SPSS Inc., Chicago, IL, USA).
3
Propensity Score Matching in Acetaminophen Regimen
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Statistical analysis was performed using Statistic Package for Social Science (SPSS) version 19.0J for Windows (SPSS Inc., Chicago, IL). Intergroup comparisons were performed using the Pearson Chi-square test, McNemar test, Fisher exact test, or Mann–Whitney U test, as appropriate. In this study, we performed propensity score matching to minimize the selection bias between patients treated before and after the introduction of the acetaminophen regimen. The propensity score matching was calculated from a multivariate logistic regression model, including age, sex, body mass index (BMI), the American Society of Anesthesiologists (ASA) score, tumor staging, the extent of lymphadenectomy, operative approach, and operative procedure. With propensity score estimated, 96 pairs of patients before and after the introduction of the acetaminophen groups were matched using a 1 : 1 nearest neighbor matching algorithm. For all analyses, differences were considered statistically significant when P < 0.05.
4
Statistical Analysis Methods Comparison
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Pairwise differences in proportions and medians were analyzed by the chi-square test, Fisher’s exact test, or Mann-Whitney U test, as appropriate. All statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 19.0J for Windows (SPSS Inc., Chicago, IL). For all analyses, differences were considered statistically significant at p < 0.05.
5
Statistical Analysis of Parotid Lesions
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Statistical software (SPSS, version19.0 J for Windows; Chicago, IL) was used to analyze the raw data. The normal distribution of the measured data was analyzed by Kolmogorov-Smirnov test. The independent-samples t test was performed to assess tumor diameter, ADC values, and ADCNormalized values between benign and malignant lesions. The Mann-Whitney U test was used to analyze the significance of ordinal date of ITSS grading between the 2 groups. Fisher’s exact test was used for statistical analysis of morphology, margin, deep lobe of parotid invasion, retromandibular vein invasion, swollen lymph nodes, and the cystic regions between the benign and malignant lesions. Based on a descriptive analysis, we used the logistic regression model for regression statistical analysis. The receiver operating characteristic (ROC) curve was used for analyzing results and determining cut-off values. The sensitivity (SE), specificity (SP), and area under the curve were calculated from the ROC curve to identify benign and malignant lesions. P<0.05 was considered statistically significant.
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