The data obtained were entered into and analyzed using SPSS for Windows software version 22. Descriptive statistics were used to determine the associations between maternal obesity, excessive gestational weight and adverse pregnancy outcome using the Pearson Chi-squared (χ2) test or Fisher exact test for categorical variables as appropriate. Multiple regression analysis with adjustment for potential confounders was done for variables that had statistical significance on bivariate analysis, ‘p’ values of less than 0.05 was accepted as statistically significant.
Spss software for windows version 22
Manufactured by IBM
Sourced in United States
SPSS software for Windows version 22.0 is a comprehensive data analysis and statistical software package. It provides a range of tools for data management, analysis, and presentation. The core function of the software is to enable users to perform a variety of statistical analyses, including descriptive statistics, regression, and hypothesis testing.
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Lab products found in correlation
Prism software, by GraphPad (4 mentions)
Prism 7, by GraphPad (2 mentions)
Stata ic v12, by StataCorp (1 mentions)
Statistical packages for social sciences, by IBM (1 mentions)
Stata version 14, by StataCorp (1 mentions)
Spss software for windows version 17, by IBM (1 mentions)
Spss statistic, by IBM (1 mentions)
Stata se 12, by StataCorp (1 mentions)
Pymol software, by Schrödinger (1 mentions)
Prism version, by GraphPad (1 mentions)
139 protocols using spss software for windows version 22
1
Maternal Obesity and Adverse Outcomes
2
Accuracy of Optical Diagnosis of T1 Cancers
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The primary endpoint was assessment of accuracy of optical diagnosis of T1 cancers by colonoscopy experts, general gastroenterologists, and gastrointestinal fellows.
Normally distributed data were described with the mean and standard deviation. Sensitivity and specificity and their 95 % confidence intervals (CI) were calculated from the cases with benign/malignant histopathology results separately. Test results for optical diagnosis were used as a dependent variable and level of experience (experts, generals gastroenterologists and GI fellows) was used as a covariate as dummy variables. The positive predictive value (PPV) and negative predictive value (NPV) and their CI were calculated for cases with benign/malignant optical diagnosis separately. Results of histopathology were used as a dependent variable and level of experience was used as a covariate as dummy variables. For fellows, this was also described according to their level of experience (in years of training). The calculation was done by using logistic regression parameter estimates from generalized estimating equation (GEE) with exchangeable correlation structure 18 (link). SPSS for Windows software version 22 (SPSS Inc, Chicago, Ill) and STATA/IC V12 (Statacorp: College Station, Texas, USA) were used for analysis.
Normally distributed data were described with the mean and standard deviation. Sensitivity and specificity and their 95 % confidence intervals (CI) were calculated from the cases with benign/malignant histopathology results separately. Test results for optical diagnosis were used as a dependent variable and level of experience (experts, generals gastroenterologists and GI fellows) was used as a covariate as dummy variables. The positive predictive value (PPV) and negative predictive value (NPV) and their CI were calculated for cases with benign/malignant optical diagnosis separately. Results of histopathology were used as a dependent variable and level of experience was used as a covariate as dummy variables. For fellows, this was also described according to their level of experience (in years of training). The calculation was done by using logistic regression parameter estimates from generalized estimating equation (GEE) with exchangeable correlation structure 18 (link). SPSS for Windows software version 22 (SPSS Inc, Chicago, Ill) and STATA/IC V12 (Statacorp: College Station, Texas, USA) were used for analysis.
3
Survival Analysis of KMO Expression in CRC
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All calculations were performed using SPSS for Windows software, version 22 (SPSS, Chicago, IL, USA). A receiver operating characteristic curve (ROC) analysis was used to select the optimal cutoff values of KMO expression, including proteins (KMO H-score, Supplementary Figures 2A,B ) and transcripts (KMO RSEM Supplementary Figures 2C,D ), for defining low vs. high expression of KMO. The KMO expression level as test variable and patient's survival status as state variable were used to calculate coordinates of the ROC curve, sensitivity, and 1-specificity using SPSS software. The Youden index (22 (link)), the maximum value of sensitivity+specificity-1, was selected the optimal cutoff values. For survival analysis, overall survival (OS) and disease-free survival (DFS) curves of patients with CRC were plotted using the Kaplan–Meier method and compared using the log-rank test. The association between KMO expression and clinicopathological parameters was analyzed using contingency tables and the chi-square test. Statistical comparisons were performed using non-parametric tests, and statistical significance was defined as a P < 0.05.
4
Statistical Analysis of Quantitative Data
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Data were analyzed using SPSS for Windows software version 22 (SPSS Inc., Chicago, IL, USA). Means and standard deviations, proportions, and percentages were determined as appropriate. Test of associations between categorical variables was done using the Chi-squared test and 95% confidence intervals (CI) documented. The association between two continuous variables was determined using the correlation test. Probability (P) = 0.05 was considered statistically significant.
5
Perception towards IPV Screening
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The data obtained were analysed using the Statistical Package for Social Sciences (SPSS) for Windows software version 22 (SPSS 22, Chicago).19 Descriptive data were presented using tables and charts. The prevalence of IPV experience was summarised using proportions. Associations between the categorical independent variables and perception towards screening for IPV were assessed with the chi-square test. Multivariate regression analysis was done to identify independent predictors of perception towards screening for IPV. The level of significance for all the tests was 5% (95% CI).
6
Metabolic Syndrome Characteristics Analysis
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Descriptive statistics were used to summarize demographic, anthropometric, biochemical and clinical data of the study sample. Parameters with normal data distribution were reported as mean with standard deviation, while others were reported as the median and interquartile range (IQR). To compare the differences in clinical, metabolic, sociodemographic and anthropometric characteristics according to MetS status, statistical analyses to compare two independent groups were used namely Chi-square test for categorical data and independent t-test for continuous data. Statistical significance was taken as a p-value of less than 0.05. The relationship between characteristics of study sample and metabolic syndrome was also tested using multiple logistics regression with metabolic syndrome status as a dependent variable (outcome) and sociodemographic and clinical variables as covariates. All statistical analysis was conducted by using IBM SPSS for Windows software, version 22.0 (IBM Corp, Armonk, NY, USA). There were no missing data in this study for all variables.
7
Potentially Inappropriate Medications and Outcomes
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Baseline profile data including age, sex, household status, marital status, CCI, number of medications, functional status, and nutritional status are described as counts and percentages. The periods after enrollment to first hospitalization and death during follow-up were estimated for patients with or without PIMs categorized according to the Beers Criteria and STOPP-J. A multivariate Cox regression model was used to test for the association between PIMs, mortality, and hospitalization after adjustment for age, sex, CCI, Barthel Index, MNA-SF, and polypharmacy. All statistical analyses were conducted using SPSS for Windows software version 22.0 (IBM Corp., Armonk, NY, USA). A two-tailed p value < 0.05 was considered statistically significant.
8
Peripheral Refraction Analysis Protocol
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The data were analyzed using the Statistical Package for the Social Sciences (SPSS for Windows software version 22.0; IBM Corporation, Armonk, NY, USA). Normal distribution of variables was assessed using the Kolmogorov–Smirnov test (P-values <0.05 indicated that the data were not normally distributed). The results were presented as mean ± standard deviation, and minimum and maximum value.
Relative peripheral refraction (RPR) was calculated by subtracting central refraction (spherical equivalent) from each peripheral refraction measurement. The univariate analysis of variance was conducted to assess the differences between study groups, analyzing the interactions of peripheral refraction or RPR. Peripheral refraction and RPR were plotted against the visual field angle for each study group (P-values of <0.05 were considered statistically significant). Homogeneity of variances was assessed with Levene’s test (P-values of <0.05 were considered statistically significant).
Relative peripheral refraction (RPR) was calculated by subtracting central refraction (spherical equivalent) from each peripheral refraction measurement. The univariate analysis of variance was conducted to assess the differences between study groups, analyzing the interactions of peripheral refraction or RPR. Peripheral refraction and RPR were plotted against the visual field angle for each study group (P-values of <0.05 were considered statistically significant). Homogeneity of variances was assessed with Levene’s test (P-values of <0.05 were considered statistically significant).
9
Preoperative and Postoperative Evaluation
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The collected data from all groups were imported to Statistical Package for Social Sciences (SPSS) for Windows software, version 22.0 (IBM Corp.; Armonk, NY, USA). Descriptive analyses were performed to calculate the mean and standard error of variables in each group. An exploratory test (Kolmogorov– Smirnov) test revealed normal distribution of the data; therefore, a paired t-test was used to explore statistical significance pre- and post-operatively. Percent change for the groups was averaged for all measurements. The confidence interval was set to 95% and p<0.05 was considered statistically significant.
10
Genetic Association Analysis of Coronary Artery Disease
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The “Statistical Package for the Social Sciences (SPSS)” for Windows software, version 22.0 (IBM Corp., Armonk, NY, USA) and the R version 3.5.1 (R Studio Version 1.2.1335) were applied for data analysis. Genotype analysis and Hardy–Weinberg equilibrium (HWE) calculation in patients and controls were estimated using SNPStats software (https://www.snpstats.net/ ) (last accessed 14 April 2021). Adjusted odds ratio (OR) and 95% confidence interval (CI) were calculated for each genetic association model (allelic model, homozygote/heterozygote comparison, dominant, and recessive models) [29 (link)]. Categorical variables were quoted as frequencies and percentages and were compared using the chi-square (χ2) or Fisher’s exact tests where appropriate. Continuous data are presented as mean ± standard deviation (SD) and were compared using Student’s t-test if the data distribution was parametric. Otherwise, Mann–Whitney U (MW) and Kruskal–Wallis tests were applied. Spearman’s rank correlation coefficient was run for correlations analysis. A two-tailed p-value less than 0.05 was considered statistically significant. Stepwise logistic regression was performed to detect independent predictors of CAD. The ggplot2 package was used for multivariate analysis.
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