Statistical package for social sciences version 20.0 for windows

For the statistical analyses, the statistical program Statistical Package for Social Sciences version 20.0 for Windows (SPSS, Chicago, IL, USA) was used. The distributions of continuous variables were evaluated by Kolmogorov Smirnov test. The variables were compared with one way analysis of variance (ANOVA) test and the nonparametric Kruskal–Wallis test depending on normal or non-normal distribution, respectively. The continuous variables were presented as mean ± standard deviation (SD). Categorical variables were presented as n (%) and compared with the Pearson’s Chi-squared or Fisher exact test on the basis of sample size. Post hoc pairwise comparisons were performed using the Bonferroni correction. The result was considered significant if the P-value was <0.05.

The interval from start of ritodrine therapy to delivery was analyzed using the Kaplan-Meier survival data analysis method (log-rank test). Cox’s proportional-hazards model was used for exploratory multivariate analysis. Categorical variables were analyzed using the chi-squared test or the Fisher’s exact test. A multivariable logistic regression model was used to identify independent predictors using factors with a p-value < 0.1 in univariate analysis. The fit of the prediction model was assessed using the Hosmer-Lemeshow goodness-of-fit test. Discrimination of the model was further assessed via area under receiver operating curve (AUROC) analysis, which assessed the ability of risk factors to predict ritodrine-induced ADEs. On the basis of Rozenberg et al.’s study^{19 (link)} and the assumption that overall observed MAFs of chosen SNPs were 30%, the post-hoc power analysis was conducted with PROC Power of SAS 9.4 (SAS Institute, Cary, NC, USA). All statistical tests were conducted with a two-tailed alpha of 0.05. The data were analyzed using the Statistical Package for Social Sciences Version 20.0 for Windows (SPSS, Chicago, IL, USA).

This trial was designed according to the Simon “optimal” design for phase II trials and aimed to determine whether rituximab-maintenance following R–CVP could improve PFS [33 (link)]. Based on literature analyses [7 (link), 24 (link)], the baseline 3-year PFS rate was expected to be 50%, with an anticipated treatment difference of 20%. Assuming a drop-out rate of 10%, a total of 47 patients were required to achieve a power of 80% to detect a 20% treatment difference with an alpha of 0.05. PFS was defined as the time R–CVP treatment started to the first recorded incidence of relapse, disease progression, death due to any cause, or last date of follow up for the enrolled patients who did not progress.
The intent-to-treat population (for efficacy analysis) and safety population (for safety analysis) both included enrolled patients who received at least one dose of rituximab-maintenance therapy. Time-to-event data were estimated using the Kaplan–Meier method. The Cox proportional hazards model was used to estimate the hazard ratio (HR) and the corresponding 95% CI with regard to the low-risk group. All reported P values were two-sided, and a P value < 0.05 was considered significant. All analyses were conducted using the Statistical Package for Social Sciences version 20.0 for Windows (SPSS Inc., Chicago, IL, USA).

The summarized statistical results of the baseline characteristics of patients were expressed as counts and percentages for the categorical data, and means with standard deviation and medians with interquartile ranges (IQR) were determined for continuous variables with approximately normal distributions. Logarithmic transformation of variables with a skewed distribution (TG, UPCR, and CRP) was applied in analyses. A multivariable linear regression analysis was utilized to evaluate the relationship between HbA1c and other variables. HRs and 95% CIs from Cox proportional hazard model were stratified by HbA1c and used to estimate relative risks for composite renal outcomes and all-cause mortality. The rate of kidney function decline per year was assessed using the slope of eGFR obtained from a generalized linear mixed model. Each outcome was allowed to occur only once per participant. Covariates considered for possible confounders were used for adjustment. These included age, sex, eGFR, log-UPCR, CVD, cancer, severe liver disease, smoker, hypertension, malnutrition–inflammation, Hb, albumin, log-CRP, phosphorus, BMI, WC, mean BP, HDL, and log-TG. A p value of <0.05 was considered to be statistically significant. Statistical analyses were conducted using Statistical Package for Social Sciences Version 20.0 for Windows (SPSS Inc., Chicago, IL, USA).