The data were analyzed using SPSS software version 13.0 for Windows (SPSS Inc., Chicago, IL, USA). Comparison of liver enzyme values between normal and overweight patients was performed using the independent t-test. The results reported as mean ± standard deviation (SD). The P-values less than 0.05 were considered significant and a power of 80% was considered in the analysis. All reported P-values were two-tailed. Also for multivariable analysis, linear regression was performed between liver enzymes and BMI, age, age of diagnosis weight and height.
Spss software version 13.0 for windows
Manufactured by IBM
Sourced in United States
SPSS software version 13.0 for Windows is a statistical analysis software package that provides a comprehensive set of tools for data management, analysis, and reporting. The core function of this software is to enable users to perform a wide range of statistical procedures, including descriptive statistics, regression analysis, and advanced modeling techniques.
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Lab products found in correlation
28 protocols using spss software version 13.0 for windows
1
Liver Enzymes in Overweight Patients
2
Statistical Analysis of Experimental Data
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All statistical analyses were performed with SPSS software version 13.0 for windows (SPSS, Inc., Chicago, IL, USA). Data are presented as the mean ± standard deviation. One-way analysis of variance with Bonferroni's correction was used to compare the statistical difference between multiple groups. The Least Significant Difference test was used for comparison between groups. P<0.05 was considered to indicate a statistically significant difference.
3
Survival Analysis of Oncological Outcomes
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Quantitative data are shown as mean ± SD, and numbers and percentages are provided for qualitative data. Percentages were compared using chi-squared tests, and Student’s t-test was used for continuous variables. Univariate and multivariate analyses were performed using the Cox proportional hazards regression model. The results are reported with hazard ratio (HR) and 95% confidential intervals (CI). The OS and DFS curves were determined by the corresponding Kaplan–Meier curves. All tests were two-sided, and p values <0.05 were considered statistically significant. Statistical analysis was performed using SPSS software, version 13.0 for Windows (SPSS Inc., Chicago, IL, USA).
4
Statistical Analysis of Treatment Effects
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The statistical analyses were performed with SPSS software, version 13.0 for Windows (SPSS Inc., Chicago, IL). Values of all variables are presented as mean and standard deviation. One-way analysis of variance (ANOVA) with Tukey’s HSD as posthoc test and LSD-t test were used to determine the effects of different treatments. A P-value <0.05 was considered as the level of statistical significance.
5
Depression Impact on Cancer Patient Outcomes
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The measured values of self-rated depression scale before chemotherapy were dichotomized using a Z-SDS score of 50 as the cutoff. Therefore, patients were divided into two groups (depression and non-depression). Descriptive statistics and frequency distributions were generated from patients' demographics and disease-related characteristics. The QOL and SDS before and after chemotherapy were analyzed using independent t test. Comparisons between depression and non-depression groups were performed using the Mann-Whit U test for continuous variables according to normal distribution and X2 for categorical variables. Pearson and Spearman correlation coefficient was used to determine the relationship between depression score and demographics, clinical characteristics, and treatment response. Survival analysis was performed using Kaplan-Meier estimate 36 (link). P values less than 0.05 were considered to be significant. All analysis was performed with SPSS software version 13.0 for Windows (SPSS, Chicago, IL).
6
Comparative Statistical Analysis Protocols
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The statistical analyses were performed with SPSS software, version 13.0 for Windows (SPSS Inc., Chicago, IL). Values of all variables are presented as mean ± SEM. One-way analysis of variance (ANOVA) with Tukey’s HSD post-hoc test and LSD-t test were used to determine the effects of different treatments. A P-value < 0.05 was considered statistically significant.
7
Comparative Analysis of Novel Therapeutics
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All data are presented as the mean ± standard deviation. Data were analyzed using SPSS software version 13.0 for Windows (SPSS, Inc., Chicago, IL, USA). Statistical analysis of the data was performed by ANOVA followed by Tukey's post hoc test. P<0.05 was considered to indicate statistically significant differences.
8
Triplicate Analyses with ANOVA
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All the analyses were performed in triplicates. Experimental data were reported as mean and standard deviation and subjected to analysis of variance (ANOVA). The significance of differences (p < 0.05) among samples was determined by Student’s 𝑡-test with SPSS software version 13.0 for Windows (SPSS, Inc., Chicago, IL, USA).
9
Postoperative Pain Management Protocol
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Data were registered in an electronic database (REDCap 7.6.10 - © 2018 Vanderbilt University) and presented as mean±standard deviation (SD), or median (minimum - maximum) for continuous variables. Daily pain scores and opioid consumption in the CPNB period were compared to the pre-CPNB period through the Friedman test, using SPSS software version 13.0 for Windows (SPSS Inc., Chicago, IL, USA).
10
Testosterone and Prostate Volume Association
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We defined a low testosterone level as < 300 ng/dL. Clinical characteristics were compared between subjects with and without low testosterone comparatively by using independent t-tests for continuous variables and chi-square tests for categorical variables. The Shapiro-Smirnov test was used to verify whether the data fit a normal distribution, all continuous variables that did not conform to the normal distribution were analyzed according to the distribution, and all non-normal variables conformed to the normalized data after transformation. Age-adjusted linear regression models were used to evaluate the associations of related factors with PV values. Logistic regression models adjusted for only age or age and insulin were used to assess associations of low testosterone and PV levels and to calculate odds ratios (ORs). Furthermore, we divided subjects into four groups based on the quartiles of serum levels of testosterone to investigate whether there was a linear difference in PV values among the groups by using ANOVA. The data were analyzed using SPSS software version 13.0 for Windows (SPSS Inc., Chicago, IL, USA), and two-tailed P values < 0.05 indicated statistical significance.
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