Spss statistics package

IBM SPSS Statistics package was used for all analyses (SPSS Version 26.0; IBM Corp., Armonk, NY). Descriptive analyses were used to report the participant demographics. The within-group results of the PSQI and the PSS were analyzed using the Wilcoxon signed rank test and type I error was set at 5%. We hypothesized an improvement at posttest with significant differences when compared to pretest. Cohen's d was used as the estimate for effect sizes. Results were interpreted as <0.2, trivial/negligible effect; 0.2-0.4, small effect; 0.5-0.8, moderate effect; >0.8, large effect (Cohen, 1998 ). In addition, we calculated the median and interquartile ranges (IQR) for each PSS individual question pretest to posttest, as well as Wilcoxon analyses, to provide insight into the possible stressors during the pandemic.
Based on previous literature, we utilized anticipated effect sizes between 0.60 and 0.76 (Greeson et al., 2014 (link); Huberty et al., 2019 (link)). Through the power analysis calculation planning for power at 80% and a one-tailed analysis, this indicated a range of participants, then increased to account for a potential 20% attrition rate, to aim to enroll from 17 to 20 participants.

The texture features in tumor and normal pancreas were compared using a Mann–Whitney rank test. A Wald test with Cox regression model was used to test for associations between each texture feature and survival. A two-sided p-value of less than 0.05 was considered statistically significant. Receiver operating characteristics (ROC), including area under the curve (AUC), was used to study the prognostic value of each texture parameter. The medians were used for Kaplan-Meier plots. Data management and statistical analysis were conducted using IBM SPSS Statistics package (version 23, SPSS Inc., Chicago, IL, USA).