Stata 13

The descriptive analyses were calculated the mean and standard deviations for continuous and percentages for categorical variables. The Kurtosis and Skewness test using Stata 13.0 software was performed to assess the normality of the data.
Energy expenditure associated with physical activity was assessed in the 1st hour of accelerometer usage, which corresponds to the period of the exercise protocol for moderate and vigorous sessions, and over the course of six days. Cumulative total energy expenditure was estimated from accelerometer data for 24, 48, 72, 96, 120 and 144 hours. Differences between sessions for energy expenditure in the first hour were performed by using a one-way ANOVA followed by a post-hoc Scheffé test. Differences in daily and accumulated energy expenditure between exercise sessions was performed using linear mixed models, which takes into account the correlations between repeated measures over time. The model incorporated a quadratic term (time X time) for those analyses which demonstrated a non-linear change (p<0.05). We tested the covariance structure of the models and the unstructured covariance matrix appeared to be the most adequate for these data. All of the analysis was performed in SAS 9.3 (Statistical Analysis System, USA) and Stata 13.0 software. Statistical significance was set at p<0.05 for all analyses.

The pooled HR at a 95% CI was analyzed using Review Manager 5.3 to evaluate the association of NF-κB expression with NSCLC patient survival, and pooled odds ratio (OR) with 95% CI was analyzed to assess the association between NF-κB expression and clinical features. Engauge Digitizer version 4.1 was used to get the values of HRs and 95%CI from survival curves. X²-based Q test and I² test were used to evaluate the heterogeneity among studies. Significant heterogeneity was considered at P<0.05 and I²>50%, and the random-effects model was utilized. Moreover, a sensitivity analysis was conducted to test the consistency of the combined results by Stata13.0. Additionally, publication bias among selected studies was assessed by Begg’s and Egger’s test using Stata13.0. Statistical significance was considered at p<0.05.

Data are displayed in box plots or histograms generated using PRISM 6.0 software. Means, medians, interquartile ranges and s.d. were calculated from three independent experiments performed in triplicate, using STATA 13.0 software. In all experiments a minimum of 100 cells per time point per condition was quantified. The P-values were calculated using two-tailed, paired Student's t-tests or one-way ANOVA in STATA 13.0. For all graphs, significant values are displayed as follows: *P≤0.05, **P≤0.01, ***P≤0.001.

Meta-analyses and subgroup analysis were performed using Review Manager 5.3. Dichotomous variables were presented as odds ratios (OR; with hemosiderin excision (hemosiderin (-)) versus without hemosiderin excision (hemosiderin (+)). Heterogeneity was evaluated by the I² value. A fixed effect model was used if the I² value was less than 50%; otherwise, a random effect model was adopted. We set significance at P = 0.05. In addition to visual inspection of funnel plots using RevMan 5.3, the STATA 13.0 software was also used to perform the Begg’s test [24 (link)] and Egger’s test [25 (link)] methods to detect potential publication bias. Moreover, sensitivity analysis was performed using STATA 13.0.

Analysis was done using STATA 13.0. Three stages were undertaken, which included; first, with the use of frequency distributions, we created descriptive summaries on women’s demographic and socio-economic characteristics across all the regions of Uganda. Second, analysis of variation in contraceptive nonuse by women’s demographic and socio-economic factors across all the regions of the country was done through a cross-tabular analysis with relationships investigated using Pearson Chi-square test. Third, net-association of women’s demographic and socio-economic characteristics on contraceptive nonuse was done with a logistic regression analysis to obtain the likelihood estimates of contraceptive nonuse among women across all the regions of Uganda. Logistic regression was adopted due to the nature of the modelled outcome variable (binary outcome). Odds ratios (OR’s) with 95% confidence interval were adopted in the presentation of study findings. Relationships with p values < 0.05 were considered statistical significant; additionally, p value < 0.001 indicated very strong relationships, p value < 0.01 showed a strong relationship, and p value < 0.05 indicated moderate relationships [31 ]. Archer-Lemeshow goodness of fit test was adopted in testing the suitability of the regression model using STATA 13.0 software [32 (link)].

Data analysis used IBM SPSS Statistics version 21 (SPSS Inc., Chicago, IL, USA). To adjust for the effect of the cluster design, data were reanalyzed using STATA 13.0 with survey command. The P-value for statistical significance was 0.05. Cohen’s kappa and Intra class correlation coefficient (ICC) assessed test-retest reliability for 194 students at a time interval of one week. Cronbach’s alpha assessed internal consistency reliability. We examined construct validity by comparing the OHIP-14 and OIDP scores of groups that differed in their global measures of oral health and health status and by estimating the correlation and agreement between the two OHRQoL measures. Moreover, we assessed construct validity by estimating differences in OHIP-14 and OIDP between groups according to socio-economic and behavioral characteristics. For the purpose of cross-tabulation and multiple variable logistic regression analysis, the OIDP SC score (0–8) and the OHIP-14 SC score (0–14) were dichotomized to produce the categories 0 = no daily performance affected, and 1 = at least one daily performance affected. The distribution of the OIDP SC and OHIP-14 SC scores supported this cut-off point. We then reanalyzed the data using Poisson regression with robust variance estimation in STATA 13.0.