Statistical methods
Juvenile Arthritis, active joints, JIA, pain
JUVENILE ARTHRITIS, DISEASE, DISEASE, PGA
Descriptive statistics were used with mean and standard deviations (SDs) for continuous variables and absolute frequency percentage for categorical variables. Potential differences in VAS pain scores over time between treatment arms were compared using linear mixed models with random intercept and random slope for visits clustered within patients. The third arm was treated as reference arm since we hypothesized that arm 3 would be superior compared with arm 1 or arm 2, based on earlier research [Additionally, a third mixed model was used in an exploratory analysis to assess the ability of several baseline characteristics to predict the chance of high pain levels during follow-up. For this model multiple imputation was used to deal with missing values of variables with missing data from patients who were still in follow-up, as well as data from the 2 patients that were lost to follow-up and performed based on predictive mean matching (with 5 observations to draw from, resulting in 30 imputation sets). Imputation variables were treatment group, age at inclusion, sex, NSAID use, VAS pain, PGA, VAS patient/parent general wellbeing, VAS disease activity, symptom duration at diagnosis, Juvenile Arthritis Disease Activity Score 10 (JADAS10), number of active joints, number of limited joints, Child Health Assessment Questionnaire (CHAQ) score, CHQ- PhS, CHQ-PsS, JIA category, Erythrocyte Sedimentation Rate (ESR), baseline ESR and all baseline outcome measures. We first evaluated the possible predictive variables in a univariable model and subsequently added them to a multivariable model and evaluated its predictive value for reaching a lower VAS pain during follow-up.For all statistical analyses 