Statistical analysis
chronic kidney disease
ADVERSE EVENTS, ADVERSE EVENT, SECONDARY
On the basis of existing information on LVMI and change in LVMI at annual intervals at proposed blood pressure levels in children with chronic kidney disease, the original sample size was set to detect a difference in change in mean LVMI of more than 3·1 g/mThe primary analysis was the comparison of the outcome measures, assessed according to allocation on an intention-to-treat basis, defined as all the children who underwent randomisation irrespective of the blood pressure reached. No allowance was made for multiple testing of outcomes other than the primary outcome. We analysed the mean annual rate of change in LVMI (95% CI), and the difference (95% CI) in these rates using a restricted maximum likelihood-based repeated measures approach in combination with the Newton-Raphson algorithm. The analysis included the fixed, categorical effects of treatment, clinical centre, visit, and treatment-by-visit interaction, as well as the continuous, fixed covariates of baseline value of the outcome measure and baseline value-by-visit interaction. We used a common unstructured covariance structure to model the within-patient errors. The Kenward-Roger approximation was used to estimate denominator degrees of freedom. Significance tests were based on least-squares means using a two-sided α value of 0·05 (two-sided 95% CIs).This model accounts for the baseline and all subsequent follow-up measurements, and enables the inclusion of participants with missing measurements. It assumes that the outcome measure changes linearly over time. Patients were followed up for between 1 year and 5 years from baseline and, like all linear mixed models, the model assumes that when data were missing, they were missing at random. We also did a post-hoc sensitivity analysis that relaxes the assumption of a linear change of outcome variable over time was as follows: we estimated mean LVMI (or other outcome measures), with 95% CIs, in each treatment group at each timepoint (from 1 year to 5 years), along with the absolute difference in these means (95% CIs), using a linear mixed-effects model for repeated measures. Subgroup analysis for the primary outcome by age (dichotomised according to the median) and sex was additionally performed post hoc.We did a further post-hoc sensitivity analysis to evaluate the possible influence of missing values for the primary and secondary outcomes. Multiple imputation was done using Markov chain Monte Carlo with the following variables used to impute the missing values: clinical centre, group, time, and baseline measurements (height, weight, BMI, body surface area, heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, and pulse pressure). 20 datasets were imputed to generate the average adjusted mean difference reported previously. Adverse events and serious adverse events are summarised by treatment group in the intention-to-treat population. A Wilson test was used to assess difference in risk for overall adverse events and serious adverse events in the intention-to-treat population. The statistical analysis plan is in the This trial is registered with ISRCTN, ISRCTN25006406.