Statistical analysis
Pain, ICH, pain, articular disorders, subscale;Knee pain, knee pain
PGA
For the exploratory clinical study, statistical analysis was performed on the full analysis set (FAS) population following the guidelines on statistical principles for clinical trials (ICH E9). Due to the exploratory nature of the study, the number of subjects to be recruited has been estimated based on a potential “medium” effect size of 0.60, which is more than the commonly observed effect of the placebo in clinical trials on articular disorders in the knee (0.50) [Quantitative variables were summarized with classical descriptive statistics (n, arithmetic mean, standard deviation, median, interquartile range) at each time point. Counts and percentages were reported at each time for qualitative variables. Median (P25-P75) was also reported for ordinal variables. Assessment of the normality of the distribution of the quantitative variable was investigated numerically by comparing mean and median and graphically using histogram and quantile-quantile plot. Shapiro-Wilk normality test completed the assessment of the normality. In results tables, quantitative variables with a normal distribution were expressed as mean (± SD). Median (Q1-Q3) was used when normality was not fulfilled.The primary target variable, KOOS global score, was analyzed using a Student t-test for paired samples. The time evolution of the following quantitative variables was analyzed using repeated-measures ANOVA model when normality distribution was fulfilled, and Friedman’s test for variables with dissymmetric distribution:
The global score of the KOOS and its five subscales: Pain, Symptom, Activity of Daily Living, Sport and Recreation function and Quality of Life (the global score was calculated by summing up the score of all five subscales);Each subscale of the 36-item Short-Form Health Survey (SF36) and the global score of quality of life obtained by summing up the score of each subscale;Knee pain at rest and while walking using the Visual Analog Scale (VAS);VAS for PGA.When appropriate, a natural logarithmic transformation was performed to achieve residuals normality assumption. Multiple pairwise comparisons between time points were performed in case of significant time evolution. Effect size between baseline and the end time point was calculated for KOOS global score and subscales, for knee pain at rest and while walking, and for PGA. Effect size calculated as Cohen’s d was obtained by dividing the mean difference by its standard deviation. For effect size associated to knee pain at rest and while walking and for effect size associated to PGA, the absolute value of the mean difference was considered.Compliance and subjects’ satisfaction were compared between the first and the second follow-up using Wilcoxon’s sum of rank test. The rate of supplementation responder was compared between the first and the second follow-up with McNemar’s test. Time evolution of the use of pain killers for knee pain (yes/no) was analyzed using a generalized linear model for repeated measures. Time evolution of the number of days of rescue treatments uptake was modeled using a Friedman’s test.Statistical significance was achieved at 95% confidence (p-value significance < 0.05). Due to the exploratory nature of the study, no multiplicity adjustment was implemented.For the 