Statistical analysis
bodily pain, pain
REGRESSION, COMPLICATIONS
A sample size of 194 in each group was needed to detect a mean difference in area under the curve of 0.33 standard deviations derived from the SF-36 bodily pain domain with 90% power and a 5% (two sided α) significance level. As observed in other clinical studies, a difference of 0.33 standard deviations in generic health status is considered clinically relevant in terms of treatment effect size in the small to medium ranges. To allow for 10% of participants with data completely missing, with no area under the curve calculable, we needed 430 participants.The analysis followed a prespecified statistical analysis plan (see supplementary appendix 1). Analyses were based on the intention-to-treat principle, with participants analysed as randomised irrespective of crossover. We analysed the primary outcome using a mixed effects regression model, with fixed effects for the minimisation covariates and a random effect for the centre. The area under the curve for each participant was generated by the trapezium rule for those with at least one time point up to 30 months post-randomisation. We imputed missing data at 18 months with multiple imputation using Rubin’s rule under a missing at random assumption. A sensitivity analysis was performed including all participants who had at least one time point up to 18 months, with multiple imputation being used for missing data at 18 months. A complete case analysis of the primary outcome was also performed for participants with a score at 18 months, and those without such a score were excluded. The area under the curve is interpreted as bodily pain over 18 months. The results of the condition specific questionnaire, SF-36 (excluding bodily pain), and persistent symptoms were analysed using a repeated measures mixed effects regression model correcting for baseline score, fixed effects for the minimisation covariates, and a random effect for centre. We measured outcomes at 3, 9, 12, and 18 months, and treatment effects were estimated from time-by-treatment interactions at each time point. Missing baseline data were imputed using the centre specific mean of that variable. We analysed complications and need for further treatment using a Poisson model adjusted for minimisation covariates sex (male, female) and age (<35, 35-64, ≥65 years) and including a random effect for the centre using robust error variance.The economic analysis was conducted according to a prespecified and agreed health economics analysis plan (available from the corresponding author on request). Mirroring the statistical analysis, the principles of the intention-to-treat analysis were followed to compare cost and QALYs between groups. Data for the 24 month follow-up were used as these would better reflect costs and consequences relevant to the economic analysis. Reliance on complete case data for cost effectiveness analysis can introduce bias unless the data are completely missing at random. We implemented multiple imputation