Spss statistics version 25

Descriptive statistics are reported as mean and standard deviation or median and range/interquartile range (IQR) for normally distributed and skewed data, respectively. Paired t-tests were used to compare kidney volume techniques and results were displayed graphically using a Bland–Altman plot [15 (link)]. Pearson correlation coefficient was used to quantify linear relationships between continuous variables. A total of 12 participants are required to detect a correlation coefficient of 0.8 with 90% power and alpha 0.05. Our decision to include 40 participants yields a power > 99.9% to detect a correlation coefficient of 0.8 at alpha 0.05. Interobserver reproducibility was measured using coefficient of variation (CoV) (calculated by the standard deviation divided by the mean) and intraclass correlation coefficient (ICC) (two-way random, average measures). One-way ANOVA was used to compare mean results across the three participant groups, with t-tests to interrogate pairs where groups differed. The mean value of the two observers is reported unless otherwise stated. All analyses were performed using SPSS Statistics Version 25.0 (Armonk, NY: IBM Corp.) and a conventional significance level of < 0.05 was used. Figures were generated using SPSS Statistics Version 25.0 (Armonk, NY: IBM Corp.) and Microsoft PowerPoint^® 2019.

All statistical analyses were performed using graphpad prism 5 (GraphPad Software, San Diego, CA, USA) and ibm spss statistics, version 25 (IBM Corp., Armonk, NY, USA). Fisher's exact test and Wilcoxon matched‐pairs signed rank test were performed and Kaplan–Meier analysis was used to estimate survival curves; PFS was calculated from the day of the administration of the first cycle of pembrolizumab until the day of documentation of disease progression or death from any cause. Overall survival (OS) was calculated from the day of treatment initiation until the date of death from any cause. Cox regression analysis was performed to investigate the risk for progression and death. P values were calculated by two‐sided tests and were considered statistically significant at the 0.05 level.

For two-group comparisons, tabulations and chi-square tests as well as t-tests were used. Analysis of variance (ANOVA) was used for simultaneously comparing graduated variables over all five fields of study. Personality related factors, such as SOC and CMQ, were defined as described in Buddeberg-Fischer et al. [26 (link)]. T-tests, confidence intervals, and Cohen’s effect size were used to assess the relative importance of the various factors influencing the choice of medical studies as opposed to other university majors. Chi2-test was used to examine gender differences.
We used content analysis, a qualitatively oriented category-guided text analysis, for the respondents’ electronic answers to the free-text question [27 (link), 37 ]. The two main steps of the content analysis were first, the development of a categorization frame with a code manual and determining operational definitions for each content category, and second, coding the text according to the categorization frame to the content categories.
For data analyses we used Stata Statistical Software: Release 16 (StataCorp, College Station, TX, USA) and IBM SPSS Statistics Version 25 (IBM Corp, Armonk, NY, USA). The significance level was set at 0.05, two-sided.

Standard descriptive statistics were used to summarize study population characteristics. Student’s t-tests or Mann-Whitney U-tests were used for continuous variables. The association between categorical variables was evaluated using the chi-square test or Fisher’s exact test. The McNemar χ² test was used to compare sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) among diagnostic procedures. Logistic regression was used to identify mortality predictors. The Hosmer-Lemeshow test assessed the goodness of fit for logistic regression models. All statistical studies were performed using IBM^® SPSS^® Statistics version 25 (Armonk, NY, USA: IBM Corp).

Patient data was de-identified prior to analysis. Pearson correlation coefficients were measured using bivariate correlations. A Bland-Altman analysis was used to determine the degree of agreement between average macular thickness measured with Optos SLO/SD-OCT and Cirrus HD-OCT. Sample means, mean differences, and 95% confidence intervals were obtained using independent sample t-tests. Hypothesis tests were 2-sided. Repeated measurements were statistically managed by averaging values from both eyes and nonparametric tests (Mann-Whitney U and Spearman’s correlation) were used to confirm statistical significance and degree of correlation. Receiver-operating-characteristic (ROC) curves were calculated by splitting MS patients into two categorical groups of decreased or normal macular thickness based on previously-established normative average and central macular thickness values [29 (link)]. Subjects with below-average values were classified as diseased while subjects with above-average values were classified as without disease. The areas under the ROC curves (AUCs) were calculated and compared with the AUC under the reference line. Analyses were performed with Microsoft Excel Version 16.32 (Microsoft Systems, Redmond, WA) and IBM SPSS Statistics Version 25 (IBM Corp., Armonk, NY).