Spss statistics software 21

Data were analyzed using SPSS Statistics software 21.0 (IBM, Armonk, USA). Results are presented as mean ± standard error of the mean (SEM). For the data showing normal distribution under the Kolmogorov–Smirnov test, groups were compared using the two-tailed Student’s t test. Non-normally distributed data were analyzed using the Mann-–Whitney test. Correlative statistics were performed using the Pearson product moment correlation coefficient. Lifespan curves were plotted using the Kaplan–Meier estimate and analyzed for significance between groups with the log-rank test. Levels of significance are *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; NS, not significant (P > 0.05). We also indicated the exact P value when tendencies but not significance were found: 0.05 < P < 0.10.

All results are expressed as the mean ± SEM. Statistical analyses were carried out with GraphPad Prism 9 or IBM SPSS Statistics software 21.0. The Shapiro–Wilk normality test was used to examine the normal distribution of the data. Differences among groups were tested by one-way ANOVA analysis. A value of p < 0.05 was considered statistically significant.

SPSS Statistics software 21.0 (IBM, Armonk, New York, USA) was used for statistical analysis of the data. The Shapiro-Wilk test, skewness, and kurtosis were used to evaluate the normal distribution of the variables [38 (link)]. Data transformation [39 (link)] was conducted for variables such as “number of medications taken” and “depression at T1” which did not show a normal distribution. General characteristics were summarized by descriptive analysis. To confirm the homogeneity of the two groups at baseline, independent t-tests and x²-tests were conducted. After confirming the homogeneity of the variables between the groups, we analyzed the differences between the two groups and at the three measurement times (T0, T1, and T2) in cognition, depression and BPSD using repeated measures ANOVA. Engagement was measured by how participants participated in the intervention and interacted with the interventionist during the RT program. Independent t-tests were conducted to determine the mean difference between the 1st and last sessions of the intervention.

Simple randomization was determined using a computer generated table of random numbers. Participants were allocated to the intervention and control groups with an equal allocation ratio using SPSS Statistics software 21.0 (IBM, Armonk, New York, USA).
The allocation sequence was also concealed from the participants, interventionists, and researchers measuring the outcomes until the participants were assigned to interventions and started the intervention. Performance bias is evitable in nonpharmacological interventions [7 (link), 13 (link)]. Enrollment of participants was performed by the researchers responsible for communicating with the daycare centers and for interviewing the participants’ family members. Allocation to each intervention group was performed by the research assistant, who was able to operate the computer program according to the researchers’ instructions. Outcome measurement was also performed by two doctoral students, and the allocation information was concealed from the doctoral students. The two trained interventionists were registered nurses, and the allocation information was also concealed from the interventionists until the intervention started.

Quantitative data are presented as mean ± SD from at least three independent experiments. All statistical analyses were performed using the IBM SPSS Statistics software 21.0. Multiple group comparisons were performed using one-way analysis of variance (ANOVA). The differences between subgroups were analyzed by two-sample Student’s t-tests. A p value < 0.05 was considered significant.

Scientific research secretaries were trained to take the collection and analysis responsibilities. Continuous variables were reported as mean (standard deviation [SD]) or median (interquartile range [IQR]). Student t test for continuous variables with parametric distribution. Mann–Whitney U test or Kruskal–Wallis H test for those with nonparametric distribution. Categorical variables were reported as numbers and percentages, and compared using Pearson χ² analyses or Fisher exact test. The predictive ability of potential factors for postoperative complication III-V was assessed by the corresponding area under the receiver operating characteristic (AUROC) curve. Youden index was utilized to choose the optimal cut-off value, which set as the value maximizing the sum of sensitivity and specificity. To identify risk factors for postoperative severe complications, all significant factors in the univariate analysis were used for multivariate analysis by the forward stepwise logistic regression. All statistical analyses were performed using IBM SPSS Statistics software 21.0, and statistical significance was set at P < 0.05, with two-tailed.