Statistical package for the social sciences spss for windows

The sample size was calculated for t-test based on a previous study [16 (link)]. The mean pain score (VNRS) at postoperative 24 h was 3.1 ± 2.1. If 40% reduction in pain compared to that in the placebo group was considered to be clinically relevant, 46 patients per group were needed at type 1 error of 5% and power (1-β) of 80%. Considering a dropout rate of 5%, a total of 96 patients were included and allocated to two groups of 48 patients per group. Data were expressed as the mean ± standard deviation for data with normal distribution, median (interquartile range) for data without normal distribution, and numbers (%) for nominal data. Student t-test or Mann–Whitney U test was used to analyze continuous variables. χ² test or Fisher’s exact test was used to compare categorical variables as appropriate. Statistical significance was considered at p-value of <0.05. IBM Statistical Package for the Social Sciences (SPSS) for Windows (version 24.0; IBM, Armonk, NY, USA) was used to perform the statistical analyses.

Statistical analyses were performed according to international standards and have been described by us and others before.^{[12 (link)]} Categorical variables were described in frequencies and percentages. Continuous variables were represented as a median, IQR, and its range. Continuous variables were compared using the Wilcoxon–Mann–Whitney U test. Contingency table was analyzed by chi-square or Fisher exact test, as appropriate. All tests were 2-sided and P values ≤.05 were considered statistically significant. Analysis was done using International Business Machines Corporation (IBM) Statistical Package for the Social Sciences (SPSS) for Windows (version 22.0; IBM, Chicago, IL), BiAS (version 11, Frankfurt, Germany), and R (R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria).

The IBM Statistical Package for the Social Sciences (SPSS) for Windows (version 24.0; IBM Corporation, Armonk, NY, USA) was used to perform the statistical analyses. The level of significance was defined as p < 0.05 if not otherwise noted, and all tests were two-tailed. Observed-case analyses were used to avoid overestimation of the treatment effect by imputing better previous outcome scores in a longitudinal study of a progressively advancing disease. One-way analysis of variance (ANOVA) with Bonferroni correction was used to compare the difference between the mean scores calculated from the continuous assessment scales and the four AT(N) biomarker profiles. To compare the quartile or quintile of individuals with the lowest values of Aβ₄₂ or the highest values of tau as the reference against all other groups, ANOVA with Dunnett t tests was performed. Independent-sample t tests were used to compare the differences between the means obtained for two groups, such as APOE genotype, and chi-squared tests were computed to analyse categorical variables. Spearman’s non-parametric correlation coefficient was calculated to investigate the presence of any linear associations between the CSF biomarker values and the rates of cognitive and functional deterioration.

We classified the countries, where respondents completed their core oncology training, into LMICs, upper-middle-income countries (UMICs) and HICs on the basis of World Bank criteria [13 ]. Our study aimed to describe oncologists’ preparedness for practice based on their training across LMICs, UMICs and HICs. Data were initially collected through Qualtrics, and then exported to IBM Statistical Package for the Social Sciences (SPSS) for Windows (version 24.0, Armonk New York, 2018). We excluded data from respondents who only completed the demographics section of the survey. Proportions for responses were reported with the number of people who responded to a particular question as the denominator. Pearson χ² tests were used to test for the difference in proportions, and the Kruskal–Wallis test was used to compare ordinal and continuous data by income stratification. We used thematic analysis for free-text responses.

For the statistical analyses, the IBM Statistical Package for the Social Sciences (SPSS) for Windows (version 22.0; IBM Corporation, Armonk, NY) was used. Descriptive analyses were conducted using percent and frequency for qualitative variables and mean with SD for quantitative variables. For comparisons of two independent groups (DLB and PDD), a Student’s t-test was used for normally distributed data and a Mann-Whitney U test was used for nonparametric data. Qualitative variables were assessed using a chi-squared test. A p-value of less than 0.05 was considered significant. Adjusted odds ratios (OR) are presented with 95% confidence interval (95% CI). All tests were performed bilaterally.

The IBM Statistical Package for the Social Sciences (SPSS) for Windows (version 22.0; IBM Corporation, Armonk, NY, USA) was used to perform statistical analyses. The level of significance was defined as P < 0.05 if not otherwise specified, and all tests were two-tailed. Parametric tests were used because of the large sample size and the approximately normally distributed continuous variables. A one-way analysis of variance (ANOVA) was performed to compare the differences between the means obtained for three or more independent groups, such as the interaction effect of sex by living status, and groups divided according to PSMS score. A t test was used to analyze two independent groups, e.g., sex, living status, and use of specific medications. Pearson’s correlation coefficient was calculated to investigate any linear associations between continuous predictors, such as survival time in NHs and age, cognitive or functional performance, or number of concomitant medications.