Spss statistics 22.0 for windows

All variables were analyzed using IBM SPSS Statistics 22.0 for Windows. The Independent-samples T test was used to analyze the measurement data and X² test count data. Fisher’s exact test was used under conditions of n < 40 or any T < 1, and Yates’ correction for continuity was used under conditions of n ≥ 40, 1 ≤ any T ≤ 5 for the 2*2 table X² test. Survival analyses were performed using the Kaplan-Meier method with the log-rank test. P < 0.05 was set as a statistically significant difference.

The cost data had a non-normal and skewed distribution. Accordingly, regression analysis of costs was performed by using a generalized linear model (GLM) with the assumption of a gamma shaped distribution of the dependent variable [23 (link)]. GLMs are generally well suited for statistical analysis of cost data, which often show a high degree of non-normality.
We used a two-step procedure. First, logistic regression with costs as binary outcome was performed in order to understand which of the factors that was associated with the highest costs. Second, a GLM model was run to explore the magnitude of the cost-driving factors. In the GLM, the cost driving factors were dichotomized and first entered separately. All models were adjusted for age, gender and education. In the joint analysis, all factors were entered simultaneously. Dementia diagnosis is included in the Charlson Comorbidity Index, but in the joint analysis, dementia was analyzed as a separate variable and, thus, was removed from the index. Results of the GLM are expressed as relative change in cost due to increase in the explanatory variable (e.g. dementia). Inclusion of site (i.e. Kungsholmen and Nordanstig) did not affect the results; therefore, this variable was not included in the analyses. IBM SPSS Statistics 22.0 for Windows (IBM corp., New York, NY, USA) was used for the analyses.

For descriptive statistics, continuous and non-normal variables were summarized as average, standard deviation (SD), median and interquartile range (IQR); 25th to 75th percentiles were calculated to measure the statistical dispersion of the data; categorical variables were summarized as frequency and percentage. The Shapiro-Wilk test was used to evaluate normality for all variables. The Kolmogorov-Smirnov test was performed to define the correspondence of each parameter with a normal or non-normal distribution. The Independent Samples t Test was used to compare the means of two independent groups, considering the level of statistical significance (p value < 0.05). The Pearson linear correlation coefficient (r) was used to investigate the strength of the association between two quantitative variables considering the level of statistical significance (p value < 0.05). The Mann-Whitney U test was used to probe the influence of categorical variables on continuous ones, considering the level of statistical significance (p value < 0.05). All tests were performed with IBM SPSS Statistics 22.0 for Windows (Chicago, IL, USA).

Statistical analysis was performed using IBM SPSS Statistics 22.0 for Windows (IBM Corp., Armonk, NY, USA). Measurement data are represented as mean ± standard deviation. The mean differences among the groups were analyzed using a one-way analysis of variance, and the comparisons of count data between the groups were tested using a chi-square test. P < 0.05 was considered statistically significant.

Statistical analyses were carried out with IBM SPSS Statistics 22.0 for Windows. The results are expressed as means ± standard deviations. Normality was tested with the Shapiro-Wilk test. A two-way ANOVA was used to show differences between study variables. The level of significance was set at p < 0.05.

All continuous variables were expressed as mean ± standard deviation, categorical variables were expressed as number (percentage). The normality of variables was tested using the Shapiro–Wilk method. Group comparisons were performed by Student’s unpaired 2-tailed t-test or by Mann–Whitney U test, as appropriate. A p value < 0.05 was considered statistically significant.
Data management and analysis were performed using IBM^® SPSS^® Statistics 22.0 for Windows^® software (IBM Corporation, New Orchard Road Armonk, New York, NY, United States).

Statistical analysis was performed with SPSS Statistics 22.0 for Windows (IBM, Armonk, NY, USA). All values were expressed as means ± standard deviations. Differences between the groups were assessed using one-way analysis of variance (ANOVA). Tukey’s honestly significant difference (HSD) test was used to determine the group that caused these differences. Additionally Student’s t test was performed to analysis of the datas between controls and test animals. A value of P < 0.05 was considered statistically significant.