Statistical data analysis was performed with IBM Statistical Package for Social Sciences—SPSS for Windows (version 20.0). Chi-square analysis was used to compare categorical variables. Kaplan-Meier survival curves were used to evaluate correlation between STn expression and cancer-specific survival (CSS) and were compared using log-rank test. Furthermore, multivariate Cox regression analysis was performed to assess the individual effect of the evaluated markers on patient’s survival and adjust to potential confounders (variables that could affect CSS of NMIBC and MIBC patients). The correlation between PI3K/Akt/mTOR pathway molecules was performed using Spearman rho test.
Statistical package for social sciences spss for windows version 20.0
Manufactured by IBM
Sourced in United States
SPSS for Windows (version 20.0) is a software package used for statistical analysis. It provides a comprehensive set of features for data management, analysis, and presentation. The core function of SPSS is to enable users to perform a wide range of statistical procedures, including regression, correlation, and hypothesis testing.
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Lab products found in correlation
5 protocols using statistical package for social sciences spss for windows version 20.0
1
Evaluating Biomarkers in Bladder Cancer
2
Demographic Data Analysis of OOR Values
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Demographic data were summarized using median and interquartile range (IQR). The proportion of OOR values based on the new RIs were compared with those of comparator laboratories. Post hoc analysis was done as per Beasley's technique, which is a post hoc method that uses multiple regression to interpret Chi-square contingency tables.[8 ] Multiple pairwise comparison between the groups, an alpha correction (Bonferroni's correction)[9 (link)] using the formula 1 − ([1 − alpha]1/t) was applied to determine significance, where “t” denotes the number of groups to be compared. Substituting t = 3 and alpha = 0.05, the new level of significance is P= 0.017. The number of OOR values between males and females was compared using Chi-squared test. Statistical significance was set at P< 0.05. Statistical analyses were performed using MedCalc version 17.2 (Publisher: Medcalc Software, Ostand, Belgium, 2017), Graphpad Prism Instat version 5 (Publisher: Graphpad Software, La Jolla, California, USA, 2007) and Statistical Package for Social Sciences (SPSS) for Windows, version 20.0 (Publisher: IBM, Armonk, New York, USA, 2011).
3
Analyzing Cancer Markers in Lesions
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Statistical data analysis was performed using the IBM Statistical Package for Social Sciences—SPSS for Windows (version 20.0). Chi-square analysis was used to compare categorical variables. Correlation between cancer associated markers expression in pre-malignant lesions and concomitant tumours whenever present was performed using Pearson correlation test. A P value of ≤0.05 was considered to be statistically significant.
4
Statistical Analyses for Experiments
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Statistical analyses were performed using the GraphPad Prism for Windows (version 5.00). The results are presented as means ± S.E.M. Comparisons between groups were performed using one-way ANOVA test, followed by the post hoc Bonferroni's multiple comparisons. The association between categorical variables was analyzed using Pearson's test in the IBM Statistical Package for Social Sciences (SPSS) for Windows, version 20.0 (SPSS Inc., Chicago, IL, USA). Significance was accepted at P less than 0.05.
5
Validating Virtual Diagnostic Clinic for Education
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All data were analysed using IBM Statistical Package for Social Sciences (SPSS) for Windows version 20.0. For all statistical tests, a significance level of p < 0.05 was used. Regarding the validation process, details pertaining to the content validity are provided in the Appendix. To compare the difference between the responses of experts and novices to each item, the Likert scale was changed to agree (4 ≥) and disagree (≤ 3) for the Fisher’s exact test, to compare the differences between rates of correct scores of experts and novices for each theme, the impressions of VDC, and their evaluation of game quality and usability for education (construct validity and face validity). We used independent sample t-test and one-way analysis of variance (ANOVA) to analyse the influence of game-playing habit, learning strategies, and VDC experiences on qualification test and performance scores of each department (concurrent validity and predictive validity). Regarding the application stage, we again used Fisher’s exact test to compare the extents of self-learning and VDC recommendation of clerkship students before and after COVID-19 epidemic. A qualitative analysis was conducted to summarise the recommendations of the students.
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