The data were expressed as mean ± standard deviation (SD). The expression levels of miRNAs in cervical cancer and matched normal tissues were analyzed by unpaired t test. The chi-square and t tests were performed to assess the relationship between miRNA expression and clinical features. Kaplan-Meier survival analysis and univariate/multivariate Cox proportional hazard regression analysis were carried out to compare each miRNA (low vs. high level) and prognostic miRNA signature (low vs. high risk). P value less than 0.05 was considered as statistical significant. The statistical analysis was performed using IBM SPSS Statistics software program version 22.0 (IBM Corp., NY, USA).
Spss statistics software program version 22
Manufactured by IBM
Sourced in United States
SPSS Statistics software program version 22.0 is a data analysis and statistical software package developed by IBM. It provides a comprehensive set of tools for data management, analysis, and presentation. The software is designed to handle a wide range of data types and supports a variety of statistical techniques, including regression analysis, factor analysis, and predictive modeling.
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Lab products found in correlation
6 protocols using spss statistics software program version 22
1
Expression Analysis of miRNAs in Cervical Cancer
2
miRNA Expression and Lung Cancer Prognosis
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The data were expressed as mean ± standard deviation (SD). The expression levels of miRNAs in lung cancer and matched normal tissues were analyzed by unpaired t-test. The chi-square and t-tests were performed to assess the relationship between miRNAs expression and clinical features. Kaplan-Meier survival analysis and univariate/multivariate Cox proportional hazard regression analysis were carried out to compare each miRNA (low vs. high level). P value less than 0.05 was considered as statistical significant. The statistical analysis was performed using IBM SPSS Statistics software program version 22.0 (IBM Corp., NY, USA).
3
miRNA Expression and Prognosis in LUAD
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We analyzed the expression levels of the miRNAs in LUAD and matched healthy tissues using unpaired t-tests. We performed Kaplan–Meier survival analysis and univariate/multivariate Cox proportional hazards regression analysis to compare the expression levels (low vs high) and prognostic significance (low risk vs high risk) of each miRNA. We considered P-values <0.05 to be statistically significant. Statistical analysis was performed using IBM SPSS Statistics software program version 22.0 (IBM Corp., Armonk, NY, USA).
4
Rab11-FIP4 Expression and Survival Analysis
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IBM SPSS Statistics software program version 22.0 (IBM Corp., NY, USA) was used to process the data. Data are presented as the means ± SEM of values from three independent experiments. Statistical significance among different groups was analysed by one-way analysis of variance (ANOVA). The correlation between Rab11-FIP4 and the clini-copathological features were assessed by χ2 test and Fisher's exact test. The Kaplan-Meier method with the log-rank test or Cox regression method was used to evaluate overall survival. A P-value <0.05 was considered statistically significant.
5
Prognostic Significance of miRNAs in KIRC
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We analyzed the expression levels of the miRNAs in KIRC and matched normal tissues by unpaired t tests. We performed Kaplan-Meier survival analysis with the log-rank method and univariate/multivariate Cox proportional hazard regression analysis to compare the expression (low vs. high expression levels) and prognostic significance (low-risk vs. high-risk) of each miRNA. We considered P-values < 0.05 to be statistically significant. Statistical analysis was performed using IBM SPSS statistics software program version 22.0 (IBM, North Castle, NY, USA).
6
Predictive Factors of Iatrogenic Injury
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All statistical analyses were carried out using the IBM SPSS Statistics software program, version 22.0 (SPSS Inc., Chicago, Illinois, USA). Continuous variables were presented as the median and range. Categorical variables were presented as numbers and percentages. Univariate analysis was performed with the Student t test for continuous variables and with the chi-square test for categorical variables. Logistic regression analysis was used to identify independent predictive factors of II by calculation of odds ratios and its 95% CI. A p ≤ 0.05 was considered statistically significant. Significant continuous variables were transformed into categorical variables using receiver operating characteristic (ROC) curves. The optimal cut-off point with the highest sum of sensitivity and specificity was chosen for each variable.
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