SPSS version 24.0 for Windows (SPSS Inc, IBM, Chicago, Illinois) was used to analyze the collected data. The Chi-square (χ2) test was used to examine the relationship between each marker's expression and clinicopathological and histological parameters. The Kaplan-Meier test was used to analyze survival data, and the log-rank test was used to compare survival curves. A 2-tailed P ≤ .05 was considered significant in all tests.
Spss version 24.0 for windows
Manufactured by IBM
Sourced in United States
SPSS version 24.0 for Windows is a software application developed by IBM for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and reporting. The software is designed to handle a wide range of data types and can be used for various statistical techniques, including regression analysis, descriptive statistics, and data mining.
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Lab products found in correlation
Stata se 15, by StataCorp (1 mentions)
Statistical software version 15, by MedCalc (1 mentions)
R software version 4, by R Project for Statistical Computing (1 mentions)
Prism 6, by GraphPad (1 mentions)
Prism v6, by GraphPad (1 mentions)
Matlab, by MathWorks (1 mentions)
R statistical package version 4, by R Project for Statistical Computing (1 mentions)
78 protocols using spss version 24.0 for windows
1
Survival Analysis of Biomarkers
2
Diagnostic Biomarkers in Kidney Transplantation
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Absolute and relative frequencies are presented for nominal variables. To evaluate prospective diagnostic power receiver operating characteristic (ROC) curves including AUC data were generated at 48 hours after kidney transplantation. We used the CHI2-Test to compare categorical variables. T-test were applied to test parametric variables between groups and Mann-Whitney U tests in the non-parametric case, respectively. Multivariate analyses were performed by using a logistic regression model and by establishing a Cox proportional hazards model for time-to-event analyses. For all analyses, a p-value <0.05 was defined as statistically significant. Confidence intervalls (CI) were calculated on a 95% level. Statistical analyses were performed using the SPSS version 24.0 for Windows.
3
Olfactory Deficits in Tourette's Syndrome
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We used the g*power software to determine sample size [41 (link)]. With an alpha of 0.05, an effect size of 0.4, as well as power of 0.8, the total sample size calculated was 34.
Data were investigated using SPSS version 24.0 for Windows (SPSS Inc., Chicago, IL, USA). P-values < 0.05 were considered statistically significant. The test of Kolmogorov-Smirnov indicated that values of the olfactory testing were normally distributed. Descriptive values are reported as mean ± standard deviation. Independent samples t-test (two-tailed) was used to compare performance in olfactory tests between TS subjects and healthy controls. In the case of statistically significant differences between TS subjects and controls, multiple linear regression analysis (“stepwise” method) was carried out in the data from the TS subjects to assess whether disease duration, smoking burden, handedness, medication intake, severity of tics, presence of urge, social impairment, depression, anxiety, ADHD, OCD, or cognitive functioning (performance in trail making test or digit span forward/backward) had an impact upon the olfactory deficit.
Although this study was not powered to discern differences among subgroups (pure TS versus TS plus), exploratory analysis is included in the results. The independent samples t-test, two-tailed, was used for this comparison as well.
Data were investigated using SPSS version 24.0 for Windows (SPSS Inc., Chicago, IL, USA). P-values < 0.05 were considered statistically significant. The test of Kolmogorov-Smirnov indicated that values of the olfactory testing were normally distributed. Descriptive values are reported as mean ± standard deviation. Independent samples t-test (two-tailed) was used to compare performance in olfactory tests between TS subjects and healthy controls. In the case of statistically significant differences between TS subjects and controls, multiple linear regression analysis (“stepwise” method) was carried out in the data from the TS subjects to assess whether disease duration, smoking burden, handedness, medication intake, severity of tics, presence of urge, social impairment, depression, anxiety, ADHD, OCD, or cognitive functioning (performance in trail making test or digit span forward/backward) had an impact upon the olfactory deficit.
Although this study was not powered to discern differences among subgroups (pure TS versus TS plus), exploratory analysis is included in the results. The independent samples t-test, two-tailed, was used for this comparison as well.
4
Comparative Analysis of Clinical Data
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To compare the demographics and clinical characteristics, SPSS version 24.0 for Windows (SPSS Inc., Armonk, NY, USA) was used. Pearson’s chi-square test or Fisher’s exact test were used to analyse categorical variables, and Mann–Whitney U-test was used to analyse continuous variables. Spearman’s rank correlation test was performed to evaluate the relationship between two variables. A p value of < 0.05 by a two-tailed test was considered to be statistically significant.
5
Factors Affecting COVID-19 RNA Shedding
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Statistical analyses were performed using SPSS, version 24.0 for Windows. Mean values and standard deviations or median values with interquartile range were used to describe continuous variables, and absolute or relative frequencies were used to describe categorical variables. We used the Student t-test and the Mann-Whitney U test for analysis of continuous variables and the χ2 test or Fisher exact test for analysis of discrete variables in bivariate analyses. Kaplan-Meier survival analysis was used to estimate the cumulative COVID-19 RNA-negativity rate. To identify risk factors associated with delayed duration of COVID-19 RNA shedding, we performed a time-dependent Cox proportional hazards model that adjusted for baseline covariates. For all analyses, probabilities were 2-tailed, and a 2-tailed P value of <0.05 was considered significant.
6
Comparative Analysis of Influenza A and COVID-19
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Data are described as the mean ± standard deviation (SD), median (interquartile range), or number (%). Comparisons of the features between the different subtypes of virus (influenza A and COVID-19) were performed using a t test to compare the mean ± SD of the continuous variables. A Mann–Whitney U test was used to compare the medians of the continuous variables, and the Fisher exact test or the Chi-squared test was used to compare the proportions. To identify the risk factors associated with severe COVID-19 infection, we performed a multivariable logistic regression analysis adjusted for the baseline covariates. Statistical analyses were performed using SPSS version 24.0 for Windows, the probabilities were two-tailed, and a two-tailed p value of < 0.05 was considered significant.
7
Predictive Risk Factors for Perforation Mortality
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The patients’ clinical data were expressed as mean ± standard deviation (SD) for the whole population, but as a median and interquartile range for study cohort or as a number and percentage. Mann–Whitney test was used for continuous parametric data. Categorical data were compared using the χ2 test or Fisher’s exact test when appropriate. Univariate and multivariate logistic regression analyses were used to investigate potential risk factors to die in perforation. Analyses were performed using the SPSS version 24.0 for Windows (SPSS Inc., Chicago, IL). A two-tailed p value of < 0.05 was considered statistically significant in all analyses.
8
Prognostic Significance of Immune Checkpoint Markers
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Statistical analyses were performed using SPSS with nonparametric statistics for IHC of IDO1, IDO2, TDO2, IL4I1, and PD‐L1 expression and clinical characteristics. Pearson's coefficient was calculated to evaluate the relationship between the expression of PD‐L1, IDO1, IDO2, TDO2, and IL4I1 and clinical characteristics. Kaplan–Meier (K–M) analysis was used to explore the prognostic value of IDO1, IDO2, TDO2, and IL4I1. The data were analyzed using SPSS version 24.0 for Windows (SPSS Inc.). Statistical significance was set at p < 0.05.
9
Evaluating Clinical Outcomes Improvement
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For each of the two outcome measures, participants were categorized as either “improvers” or “non-improvers” based on their RCI score. Improvers were compared with non-improvers in the pooled completer samples using the non-parametric Mann–Whitney U test and chi-square test, for pairs of continuous and dichotomous variables, respectively. In addition, the T3 results on the outcome measures were compared between assessors (blind/non-blind), and T1 results between completers and non-completers, using the Mann–Whitney U test. All tests were two-tailed, and to partially account for multiple testing, the significance level was set to 0.01. Values between 0.01 and 0.05 were interpreted as trends. SPSS version 24.0 for Windows was applied for all analyses.
10
Adolescents' Mobile App-Tracked Physical Activity
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Study characteristics, such as age, gender, disability, and FAS were described through ownership and usage of mobile phone apps or HRMs/SWs, and were tested by chi-square test of independence (P<.05). Mean number of PA days were reported by each participant characteristic. Binary logistic regression analyst was conducted on the overall sample through dichotomized groups of participants that reported 0-6 days of MVPA and 7 days of MVPA. Adjusted odds ratios (OR) and 95% CIs were reported to indicate the likelihood of daily MVPA. To report positive odds, girls, who were aged 15 years, with low FAS and disability were reference groups. Furthermore, adolescents who had no PA trackers were also the reference groups. SPSS version 24.0 for Windows (SPSS Inc) was used for the analyses.
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