Spss windows version 16

After data collection, each questionnaire was checked for completeness, consistency, and clarity and entered into the template and re-checked for errors. Data entry was done using EPI info version 3.5.1 statistical software and exported to SPSS windows version 16 for further processing and analysis. Attitude questions were summed and a mean score was calculated to categorize the overall attitude of the respondents. Bivariate analysis using binary logistic regression model was used to determine the association between independent predictors.
Variables found to be associated in binary at p value less than 0.05 were analyzed for multivariate logistic model using binary logistic analysis. Finally, variables which had significant association were identified on the basis of OR, with 95 % CI and p-value less than 0.05.

Data were analyzed using the Statistical Package for Social Sciences (SPSS/Windows Version 16, SPSS Inc., Chicago, IL, USA). Statistical significance was set at P < 0.05. Parametric data were expressed as mean ± SD, while the non-parametric data (qualitative) were expressed as frequency distribution: numbers and percentage of the total. Comparisons between the different non-parametric variables were analyzed using Chi- square test.

Data were analysed using the Statistical Package for Social Sciences (SPSS/Windows Version 16, SPSS Inc., Chicago, IL, USA). Statistical significance was set at P < 0.05. Parametric data were expressed as mean ± SD, while the non-parametric data (qualitative) were expressed as frequency distribution: numbers and percentage of the total. Comparisons between the different non-parametric variables were analysed using Chi- square test. Spearman’s correlation test was used to examine the association between child and parental BMI with the different variables under study.

Data were collected by face-to-face interview using a semi-structured questionnaire with the Amharic version of the socio-demographic, clinical factors, and BDI-II questionnaires.
EPI info version 3.5.3 statistical software and SPSS windows version 16 program were used for analysis. Descriptive statistics (frequencies, tables, percentages, means and standard deviation) were used for the socio-demographic and clinical variables, including individual’s response BDI-II. Binary logistic regression and odds ratio with 95 % confidence interval were used to identify the independently associated factors with depression.
All factors with a p value <0.2 in the bivariate logistic regression were entered into the multivariate model. Statistical significance was accepted at the 5 % level (p < 0.05).

Data were analyzed using the Statistical Package for Social Sciences (SPSS/Windows version 16, SPSS Inc., Chicago, IL, USA). The normality of data was tested using the Kolmogorov-Smirnov test. The data of DEXA, weight, and BMI were not normally distributed. So, non-parametric tests were used.
The 97 participant women were classified twice into 2 groups: first according to their BMI (31 normal weight and 66 overweight/obese) and second according to their BMD T score (23 osteoporotic and 74 non-osteoporotic). The parametric data were expressed as mean ± SD, where the qualitative ones were expressed as number and percentage (%). The various parametric variables of the different groups were analyzed and compared using the Mann-Whitney test for independent groups, while the frequency distribution of the vitamin D receptors among different groups (non-parametric data) were compared using the chi-square test. P < 0.05 was regarded as statistically significant for all tests.

Patient demographic and clinical characteristics were compared according to SMAD4 mutational status. Comparisons across groups were done using chi-square tests and the Fisher’s exact test. Logistic regression analysis was performed to calculate the odds ratio for development of SMAD4-positive tumors according to various demographic and clinical characteristics. The Fisher exact test was also used to determine the association of SMAD4 and TGF-β protein mutations across the CMS classifications.
Also, survival analysis was performed to determine whether the SMAD4 mutation status plays a role in clinical outcomes of metastatic CRC. OS was defined as the time from diagnosis of CRC to death owing to any cause and analyzed using the Kaplan-Meier method. Log-rank testing was used to compare survival curves across mutational statuses. Univariate and multivariate Cox regression analyses were performed to determine the association of various factors with OS in metastatic CRC cases. Variables, which were associated with poor OS in the univariate analysis, were included in the multivariate Cox regression analysis. All p values were 2-sided, and statistical significance was set at p<0.05. Statistical analyses were performed using the Stata (version 13.0) and SPSS Windows (version 16.0) software programs (SPSS Inc, Chicago, IL, USA).