Statistical methods
Descriptive statistics were calculated as means, standard deviations (SD), and 95% confidence intervals (CI). Categorical variables were summarized with relative frequency distribution. All continuous data were normally distributed and so summarized with mean and standard deviation (SD). A chi-squared test (or Fisher’s exact test if any expected count was lower than 5) was used to compare categorical values between the two groups. An unpaired t-test was performed to compare continuous variables.
Differences in RAND-36 domains between the two treatment groups were tested with the non-parametric Mann–Whitney U test. The magnitudes of group differences were estimated by calculating the effect size (ES; Cohen’s d ). ES makes it possible to interpret the importance of a group difference and facilitates comparison across different measures. ES was calculated as the mean difference between groups divided by the pooled SD20, and was judged according to the standard criteria proposed by Cohen: trivial (0.0 to <0.2), small (0.2 to <0.5), medium (0.5 to <0.8), and large (≥0.8).
In addition, propensity score matching (PSM) was used to estimate the average treatment effects between the two groups, using 1:1 nearest neighbour matching based on the propensity scores. The matched sample size was 96 (48:48). PSM was used as a sensitivity analysis to assess the robustness of the primary analysis results. Propensity scores of patients treated with no-touch or conventional SVG were estimated using a logistic regression model with age, sex, smoking, hypertension, diabetes mellitus, and creatinine level as predictors21.
The statistical analyses were performed in version 27.0 of SPSS (IBM, Armonk, NY, USA) and version 16.1 of Stata (StataCorp, College Station, TX, USA).