Significance of differences in prevalence were calculated by Chi-square test, and Fisher’s exact test, when appropriate. Significance of differences in means were calculated using t-test. To evaluate different risk factors, logistic regression models have been built for occupation at risk of UIP and for self-reported exposure to occupational hazards, adjusting for gender, age, and smoking (ever and never). Specific models stratifying for gender and adjusting for age and smoking were also build. Associations were expressed as OR with 95% CI, statistical significance was defined as a double-sided p < 0.05. Only exposure categories with five or more exposed cases were considered in the final analysis. Statistical analyses were performed using SPSS statistical software, version 20.0 (SPSS, IBM Corporation, New York, NY, USA).
Spss statistical software version 20
Manufactured by IBM
Sourced in United States, United Kingdom
SPSS Statistics is a software package used for interactive or batched statistical analysis. It is capable of handling a variety of data formats and can perform a wide range of statistical procedures including, but not limited to, data management, statistical modeling, and reporting. SPSS Statistics version 20.0 is the latest stable release of the software.
Automatically generated - may contain errors
Lab products found in correlation
R version 3, by R Project for Statistical Computing (4 mentions)
Prism 5, by GraphPad (2 mentions)
Stata version 12, by StataCorp (2 mentions)
Prism 8, by GraphPad (1 mentions)
Ms excel 2003, by IBM (1 mentions)
Simca p software, by Sartorius (1 mentions)
Spss statistics for window, by IBM (1 mentions)
Originpro software version 8, by OriginLab (1 mentions)
Prism software, by GraphPad (1 mentions)
Origin 2021, by OriginLab (1 mentions)
Prism 6, by GraphPad (1 mentions)
Epidata statistical software version, by IBM (1 mentions)
Stata se version 13, by StataCorp (1 mentions)
Prism 9, by GraphPad (1 mentions)
Sigmaplot, by Grafiti LLC (1 mentions)
Prism version, by GraphPad (1 mentions)
Prism 7, by GraphPad (1 mentions)
Stata 13, by StataCorp (1 mentions)
R statistical software version 4, by R Project for Statistical Computing (1 mentions)
Epidata version 3, by IBM (1 mentions)
344 protocols using spss statistical software version 20
1
Occupational Risk Factors for UIP
2
Evaluating Simulation Center Outcomes
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We calculated the median and interquartile range (IQR) for pre- and post-instruction test score at the simulation center. Because of the small size sample, we used the nonparametric alternative to paired T test: Wilcoxon signed Rank test. We used Mann-Whitney test to search for differences within different subgroups. Statistical significance was set at P < .05. We performed all statistical analyses using IBM SPSS statistical software, version 20.0 (IBM Corp, Armonk, NY).
3
Statistical Analysis of Clinical Outcomes
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Weighted data were used for all statistical analyses. Results were expressed as numbers (%) for categorical variables and mean ± standard deviation for continuous variables. Differences between groups were analyzed with the use of the Student's t-test for continuous variables and the χ2 test for categorical variables, respectively. Odds ratio (OR) and 95% confidence interval (CI) were used to report the trend in the rate of CT in HC over the study period. A two-tailed P < 0.05 was considered statistically significant. Statistical analyses were performed using SPSS statistical software version 20.0 (IBM Corp., Armonk, New York, USA). Comparisons of the length of stay between the two groups were performed using the independent samples Mann–Whitney U-test.
4
Comparison of Serum FSH Levels
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Statistical analysis was performed with IBM SPSS statistical software, version 20.0 (Chicago, IL, USA). All data in this study was analyzed by one-way ANOVA followed by an LSD post hoc test for multiple comparisons, unless otherwise stated. The time of cyclicity resumption and serum FSH levels were compared using the rank-sum test at different time points, compared with starting levels. A p-value of less than 0.05 was considered to statistically significant.
5
Characterizing Rabbit Muscle and Fat Composition
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A database was built using SPSS statistical software version
20.0 (IBM, Armonk, New York). First we compared the muscle weight,
volume and i-fat of the experimental rabbits at one, two and six
weeks using a one-way ANOVA with post hoc Gabriel
tests. We then compared the control rabbits with the experimental
rabbits for each time point using a one-way ANOVA with post-hoc Hochberg’s
tests, due to the uneven sample size. Finally, the distal quarter
i-fat was compared with the mid-part and proximal quarters with
2-tailed paired t-tests. A p value of < 0.05
was considered statistically significant.
20.0 (IBM, Armonk, New York). First we compared the muscle weight,
volume and i-fat of the experimental rabbits at one, two and six
weeks using a one-way ANOVA with post hoc Gabriel
tests. We then compared the control rabbits with the experimental
rabbits for each time point using a one-way ANOVA with post-hoc Hochberg’s
tests, due to the uneven sample size. Finally, the distal quarter
i-fat was compared with the mid-part and proximal quarters with
2-tailed paired t-tests. A p value of < 0.05
was considered statistically significant.
6
Predictors of Non-Invasive Ventilation Outcome
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Descriptive statistics are expressed as mean and standard deviation (SD) for continuous variables and percentage for categorical variables. Demographic and comorbidities characteristics were compared among the two groups (NIV success or failure) on continuous variables using t-tests, on ordinal-level variables using the Mann-Whitney test, and on categorical variables using χ2 tests or Fisher exact test, as appropriate.
Univariate and multivariate analysis and odds ratio (OR) were estimated with logistic regression for identifying the risk factors associated with NIV outcome, using the clinical variables illustrated in the Material and Methods. In particular a set of variables significantly associated with success in univariate analyses were included in a multiple logistic regression analysis. A P value less than 0.05 was considered statistically significant. All statistical analyses were carried out using IBM SPSS Statistical Software, version 20.0.
Univariate and multivariate analysis and odds ratio (OR) were estimated with logistic regression for identifying the risk factors associated with NIV outcome, using the clinical variables illustrated in the Material and Methods. In particular a set of variables significantly associated with success in univariate analyses were included in a multiple logistic regression analysis. A P value less than 0.05 was considered statistically significant. All statistical analyses were carried out using IBM SPSS Statistical Software, version 20.0.
7
Comparative Analysis of Telemedicine and Face-to-Face Visits
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The descriptive analysis consisted of absolute (n) and relative (%) frequencies of qualitative variables and mean ± standard deviation values of quantitative variables. Paired Student's t-test detected differences in visit time, and the Wilcoxon test revealed differences in physicians' perceptions about FF and TM. Cohen's d calculated the effect size. The chi-square adherence test assessed the percentage of absolute diagnosis agreement between TM and FF visits. The Spearman correlation test determined the relationship between variables. The analyzes were performed with IBM SPSS statistical software version 20.0 (IBM Corp., Armonk, NY, USA), adopting p < 0.05 as statistical significance.
8
Risk Factors Analysis in SPSS
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The data collected were analyzed in IBM SPSS statistical software version 20.0 (IBM, USA) for Windows with different statistical approaches. Descriptive statistics by the Chi-square test and odds ratio (OR) univariable logistic model were performed to analyze the potential risk factors and their relation to the independent variables. All analyses were carried out at a 95% confidence level with the significance level fixed at p<0.05.
9
Evaluation of Survival Outcomes
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All data were presented as the means ± standard deviation (SD), and all the experiments were repeated three times. Statistical analysis was applied by using SPSS Statistical software version 20.0 (IBM, Chicago, IL, USA). Survival curves were plotted by Kaplan-Meier analysis and compared with log-rank tests. Differences were analyzed by two-tailed Student's t-test. Values of p ≤ 0.05 were considered statistically significant.
10
Factors Influencing Cancer Treatment Modifications During COVID-19
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The distribution of continuous variables was tested for normality via the Shapiro-Wilk normality test. Univariate tests were applied based on whether the variable of interest was distributed normally (i.e., t-test, analysis of variance) or not normally (i.e., Mann–Whitney U test, Kruskal-Wallis test). Associations between categorical variables were evaluated using the chi-square test or Fisher's exact tests, as appropriate for category size. Multivariable logistic regression analysis was explored to evaluate the independent effect of hospital location, race, age, medical comorbidities, cancer status (new diagnosis vs. recurrence), and COVID-19 status, on treatment modifications (i.e., binary outcome variable) during the study period. Ethnicity was not included in the multivariable model as it was collinear with race. Statistical significance was evaluated at the 0.05 alpha level, and two-sided 95% confidence intervals were calculated to assess the precision of all obtained estimates. Data were analyzed using SPSS Statistical software Version 20.0 (IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp) and R Version 3.6.1(R Foundation for Statistical Computing, Vienna, Austria, 2019).
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