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Including baseline data as covariates did not significantly influence outcomes.
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We will perform logistic regression for the outcome measures with a dichotomous outcome, such as attainment of measures for secondary prevention using the data at the 2-year follow-up as the dependent variable and correcting for baseline using the baseline data as covariate.
To evaluate treatment effects, we used repeated measures mixed models analysis of covariance with baseline data as our covariate.
Body weight and food- and water-intake data were assessed by analysis of covariance, with treatment as a factor and baseline data as the covariate.
Linear mixed models will be used to analyse the differential change between groups on all outcomes from baseline to follow-up, using baseline data as the covariate.
A sensitivity analysis was conducted by adjusting for baseline differences between groups by using baseline data as a covariate in the mixed-effects model.
The changes in laboratory parameters from the baseline levels in the adjusted cohorts were analyzed by repeated measures analysis of variance adjusted for the baseline data as a covariate and by the post hoc Bonferroni test.
Analysis of covariance (ANCOVA) was used for the primary outcome measure and for all secondary outcome measures where baseline and endpoint data were collected and were normally distributed; with endpoint data acting as the dependent variable, baseline data as the covariate, and allocation (counselling versus waiting list) as a fixed factor.
That is, as baseline data is entered as a covariate, participants who do not complete any of the post-program assessments are not retained.
Between-group comparisons of change in measured outcomes at 3, 6, and 12 months were conducted using ANCOVA procedures; baseline data were included as a covariate.
Data were analysed according to an analysis of covariance (ANCOVA) with treatment group, allopurinol dose at baseline and BMI at baseline as covariates.
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