Stata se version 14.1 statistical software

A univariate logistic regression model was used to analyze the association between MRD negativity and factors potentially predicting relapse. Relapse-free survival (RFS) was measured from the date of CR until relapse. Patients without relapse at their last follow-up monitoring were censored on that date. The Kaplan-Meier method was used to estimate the median RFS. Univariate Cox proportional hazards regression was performed to identify the association between each of the variables and RFS. Multivariate Cox proportional hazards regression was used to model all the variables in the univariate setting. The backward selection method was used to remove variables that did not remain significant in the multivariate model (p=0.15). Hazard ratios (HRs) were generated with 95% confidence intervals (95% CI). Data were analyzed with STATA/SE version 14.1 statistical software (Stata Corp. LP, College Station, TX). The Student t-test was used to analyze the statistical significance of differences between groups, both in vitro and in vivo. All statistical tests were two-sided, and the results are expressed as the mean ± standard deviation. A p value ≤ 0.05 was considered statistically significant.

Anthropometric and nutritional parameters were measured upon presentation, prior to PD, and approximately 3 and 12 months after surgery. Differences compared to baseline were first assessed using paired t-tests. These comparisons only test for differences in the setting of complete data, which were not available for all patients. In order to control for missing data, changes over time for each anthropometric and nutritional parameter were also analyzed utilizing a multilevel mixed-effects linear regression model, which takes into account correlations between measurements and fixed effects. Beta coefficients (B) and 95% confidence intervals (CIs) were calculated.
Next, univariate Cox proportional hazards regression models were created to evaluate the association between clinicopathologic and anthropometric factors and overall survival, which was calculated from the date of tissue diagnosis to the date of death. Baseline values as well as changes in anthropometrics over time, per 10 units cm²/m², were analyzed for their potential association with survival. Hazard ratios (HR) and 95% CIs were calculated. Statistical significance was set at a two-tailed p-value <0.05. All statistical analyses were performed using Stata/SE version 14.1 statistical software (StataCorp LLC, College Station, TX).

Univariate and multivariate Cox proportional hazard models were performed to identify specific characteristics of CML-BP which can predict for survival outcomes. Variables with p-value ≤0.25 in the univariate analysis were entered into a multivariate model. Median survival and survival probabilities were analyzed using the Kaplan-Meier methods and differences calculated by the log-rank test. We used classification and regression tree (CART) analysis to identify the optimal cut off points for specific parameters associated with survival, subsequently we identified prognostic factors which could independently predict survival in CML-BP. A p-value of <0.05 was considered significant. Statistical analyses were carried out using STATA/SE version 14.1 statistical software (Stata Corp. LP, College Station, Texas).