Statview version 5

All statistical analyses were performed with Epi-info version 5 and Statview version 5 (Abacus Concepts, Berkeley, USA). Student’s t-test and one-way ANOVA were used for the comparison both between the 03 groups (D, H, and DH) and with the control group, mainly to determine the severity of CVD in the DH group. Pearson’s correlation (coefficient (r)) analysis was performed to quantify associations between CMS clusters and the other study parameters, in particular the fatty acids ratio and atherogenicity biomarkers between D and DH groups versus control group. The results were considered significant at * p < 0.05, very significant at ** p < 0.01, or highly significant at *** p < 0.001. The data of FFQ were analyzed using the computer program SPSS version 11 (SPSS Inc., Applications Guide, Chicago, IL, USA). Statistical methods for validation of FFQ include the Student’s t-test, Pearson correlation, and the kappa statistic [26 (link)].

Continuous data are presented as means ± standard deviation, while non-parametric data are presented as the median (interquartile range 25–75 percentile). Differences among groups were evaluated using one-way analysis of variance (ANOVA) with post-hoc Fisher’s PLSD test. Categorical data are expressed as incidences and percentages, and comparisons were made using the χ²-test. Multiple regression analysis was performed with REs as the dependent variable; age, gender, handgrip strength as an indicator of physical function, FOIS at admission as an indicator of swallowing status, GNRI as an indicator of nutrition status, provided rehabilitation time as an indicator of rehabilitation, and variables that had a significant correlation in the univariate regression analysis of REs were selected as independent variables. FIM scores and serum albumin levels were excluded because they closely correlated with REs or GNRI, respectively. In addition, analysis of covariance (ANCOVA) model was estimated to examine the difference in REs by BNP levels, controlling foe age and sex. P values < 0.05 were considered statistically significant. Statistical analyses were performed using STATVIEW version 5 (Abacus Concepts, Berkeley, CA).

All statistical analyses were performed with Epi-info version 5 and Statview version 5 (Abacus Concepts, Berkeley, CA). Data (normally distributed) are presented as mean ± standard deviation (SD). Student’s t-test and one-way ANOVA were used for the comparison between the CKD Black versus CKD White groups for the same dose of vitamin D supplementation: D2W versus D2B and D60W versus D60B, mainly to analyze the relationship between vitamin D deficiency in CKD Black and White groups and severity of CVD in both groups, essentially NT-proBNP and RAAS biomarkers. Pearson’s correlation analysis was performed to quantify associations between the each dose effects of vitamin D supplementation and CMet or CRenal clusters, eGRF, PTH, and atherogenicity biomarkers. The results were considered significant at *p < 0.05, very significant at **p < 0.01, or highly significant at ***p < 0.001.

Statistical analysis of group differences was performed using the χ2 test. A value of P < 0.05 was considered to be significant. Actuarial survival curves were estimated using the Kaplan–Meier method, and differences in survival between subgroups were compared with the log-rank test and Wilcoxon test. Multivariate analysis was performed using Cox-hazard model analysis. A p value of < 0.05 was considered to be significant. All p-values are two-sided in this study. All statistical analyses were performed using the software package StatView™ version 5.0 (Abacus Concepts, Berkeley, CA, US).

All data are presented as mean ± SEM and were evaluated for statistical significance using Statview version 5.0 software (Abacus Concepts Inc, Berkeley, USA). Normal distribution was verified using Kolmogorov–Smirnov test. Data for BP, GFR and proteinuria were analysed using 2 way ANOVA testing groups, postnatal age and groups × postnatal age interaction with Bonferoni post hoc analysis when appropriate. Body weight, renal structure parameters were compared between groups using one-way ANOVA with Student–Newman–Keuls comparison test for post hoc analysis. Male and female offspring were analysed separately. The interaction between group and sex was firstly evaluated and showed a sex effect for body weight, BP, parameter of renal functions and structure. A p value <0.05 was considered statistically significant.

Continuous variables are given as means with standard deviations. Non-normally distributed variables (serum ferritin, serum creatinine, serum C-reactive protein, and natriuretic peptides) were log-transformed to achieve normal distribution before analysis. Student’s t test and analysis of variance (ANOVA) with Fisher’s post-hoc test were used to test for between-group differences. Categorical variables were expressed as numbers with percentages and the Chi-square test was used to test for inter-group proportion differences. Simple regression was used to analyse the first-line associations between continuous variables.
Univariate and multivariate logistic regression models were used to identify clinical determinants of exercise capacity in patients with chronic HF. The analyses included continuous and dichotomized variables of the parameters age, gender, NYHA class, LVEF, natriuretic peptide levels, the presence of anaemia, and the presence of ID. All statistical analyses were performed using StatView version 5.0 for Mac (Abacus Concepts, Berkeley, California). All tests were two-sided. P values of <0.05 were considered statistically significant.