Sas statistical software for windows

All statistical analyses were performed using the SAS statistical software for Windows (SAS Institute, Cary, NC, USA). Spearman’s correlation coefficients were used to assess the associations between the different outcomes. In addition, we performed a multiple linear regression analysis with backward elimination (p < 0.10 to stay in the model), where stair time was the dependent variable and age, gender, BMI, knee flexor strength, knee extensor strength, knee flexion ROM, knee extension ROM and postural balance were the independent variables.

We compared the distribution of sociodemographic factors (age, sex, urbanization level, monthly income) and the proportions of comorbidities between the cohorts with and without COPD by Chi-square-test and t-test. The incidence of lung cancer in the three cohorts of patients (COPD without T2DM, COPD with T2DM, and without COPD/T2DM) was calculated in the follow-up period until the end of 2011. Univariate analysis and multivariable Cox proportional hazard models were used to estimate the hazard ratios (HRs) and 95% CIs for the risk of developing lung cancer. The multivariate models were adjusted for age, sex, urbanization level, monthly income, and comorbidities of pneumoconiosis, interstitial lung disease, pulmonary TB. We used the Kaplan–Meier analysis and the log-rank test to examine the statistical significance of the differences among the three cohorts. All data analyses were performed by SAS statistical software for windows (Version 9.1; SAS Institute, Inc., Cary, NC, USA), and the significance level was set to be 0.05.

Continuous variables are presented with mean and standard deviation or median and interquartile range values. Categorical values are presented with percentages. Student’s t-test or Wilcoxon non-parametric test was used to compare continuous variables, adapted to paired samples for the comparison of echocardiographic variables before RT and 6 months after RT. Comparison of layer-specific LS at baseline and RT + 6 months were performed, mean relative change was evaluated (Mean = V6 – V0 / V0). Specific analysis of segmental strains values according to regional level or coronary arteries territorial areas was performed, and the evolution of LS in these levels from baseline to RT + 6 months was analyzed. We compared these evolutions according to the group of exposure (“High” for patients with cardiac doses >66th percentile of dose distribution, “Low” for others). Given the exploratory nature of this work, we presented unadjusted p-values for comparisons, but in order to take into account multiple testing in these comparisons we also applied the Holm–Bonferroni method, a step-down procedure performed after conducting the multiple comparison tests. Finally, p-value < 0.05 was considered statistically significant. All statistical analysis was performed using SAS statistical software for Windows (Version 9.4 TS1M4 – SAS Institute, Cary, NC).

All statistical analyses will be performed using SAS statistical software for Windows (SAS Institute, Cary, NC). Poisson regression (injury) and linear regression (LBP) models will be performed using generalized estimating equations (Proc Genmod) and linear mixed models (Proc Mixed). An alpha level of 0.05 will be accepted as significant.

All statistical analyses will be performed using the SAS statistical software for Windows (SAS Institute, Cary, NC). For the primary outcome, use of assistive devices during the entire 1-year follow-up will be evaluated using linear mixed models. Analyses will be adjusted for use of assistive devices during baseline. Cluster is entered in the model as a random factor. We will perform all statistical analyses in accordance with the intention-to-treat principle using a linear mixed model, which inherently accounts for missing values. An alpha level of 0.05 will be accepted as significant. Outcomes will be reported as between-group least mean square differences and 95% confidence intervals from baseline to follow-up.

All statistical analyses will be performed using the SAS statistical software for Windows (SAS Institute, Cary, NC). The change in pain (0-10 scale) will be evaluated using a repeated-measures two-way analysis of variance (ANOVA) with group, time and group by time as independent variables. Participant is entered as a random effect. Analyses will be adjusted for age and pain intensity at baseline. We will perform all statistical analyses in accordance with the intention-to-treat principle using a Mixed model approach which inherently accounts for missing values. An alpha level of 0.05 will be accepted as significant. Outcomes will be reported as between-group least mean square differences and 95% confidence intervals from baseline to follow-up.

All statistical analyses were performed using the SAS statistical software for Windows (SAS Institute, Cary, NC). The change in WAI was evaluated using a repeated-measures two-way analysis of variance (ANOVA) with group, time and group by time as independent variables. Participant was entered as a random effect. Analyses were adjusted for age and WAI at baseline. We performed all statistical analyses in accordance with the intention-to-treat principle using a Mixed model approach which inherently accounts for missing values.
An alpha level of 0.05 is accepted as significant. Outcomes are reported as between-group least mean square differences and 95 % confidence intervals at 10-week follow-up.
Finally, effect sizes were calculated as Cohen’s d [31 ] (i.e. between-group differences in the WAI scores divided by the pooled standard deviation at baseline). According to Cohen [31 ], effect sizes of 0.20 are considered small, 0.50 moderate and 0.80 large.