Stata version 16 mp for windows

After checking for completeness and consistency of responses, the data was cleaned, verified, coded, and categorized. Then, the data was entered into EpiData 4.6 software for Windows and exported to Stata version 16/MP for Windows for further descriptive and analytical analysis. Analyses were stratified according to stage of cancer, comorbidity and complication status, and treatment modalities and were discussed in relation to the patient’s data with cervical cancer. Logistic regression analysis was employed to investigate the potential contributing factors of DRPs. A binary logistic regression model was used to assess the independent effects of each variable on the prevalence of DRPs. Those variables with a p-value of ≤ 0.25 during binary logistic regression analysis were fitted into a multivariable logistic regression model to identify the independent contribution of each variable. AORs, 95% CI, were built to see the strength of associations. A p value ≤ 0.05 was considered significant.

After checking for completeness and consistency of responses, the data were cleaned, verified, coded, and categorized. The data were then entered into the EpiData 4.6 software for Windows before being exported to Stata version 16/MP for Windows for further descriptive and analytical analysis. Analyses were stratified according to the stage of cancer, co-morbidity, complication status, and treatment modalities. A binary logistic regression model was used to assess the independent effects of each variable on the development of DRPs. All variables having a p-value of ⩽ 0.25 during binary logistic regression analysis were fitted into a multivariable binary logistic regression model to identify the independent contribution of each variable. For adjusted odds ratios (AORs), a 95% confidence interval (CI) was built to see the strength of associations. A p-value ⩽ 0.05 was used to declare the statistical significance of each independent variable.