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To account for possible heterogeneity between studies, we developed a meta-analysis of individual patient continuous outcome data using a random trial effect with patient-level covariates in which the original study and the observation from an individual patient were at the highest and lowest level, respectively (22).
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Ratings were checked throughout the trial by using a random sample of 24 recorded interviews with trial participants.
To validate the used publication search algorithm, two investigators independently searched for publications using the algorithm, using a random selection of 30 trials of the cohort.
We combined relative risk estimates of disease events from individual trials using a random effects model 31 (which avoids assuming that participants in the individual trials in the meta-analysis are sampled from populations in which the intervention has the same quantitative effect).
Essentially, this method simulates the accrual into a trial using a random number generator.
Those consenting to participate in the trial were randomly assigned to an intervention (exercise referral scheme) or control trial arm using a random number generator, with gender and LHB as stratification variables.
We meta-analyzed these 3 trials using a random effects model.
Relative risks for requiring allogeneic blood transfusion, transfused blood volumes, and other clinical outcomes were pooled across trials using a random effects model.
We pooled the data on ejection fraction effect size for this subset of trials using a random effects model and present them as weighted mean differences with 95% confidence intervals.
Randomisation lists were produced by the trial statistician using a random number generator programme.
Utilizing an existing systematic review, we calculated the attack rates in the trial placebo arms using a random effects model and a meta-regression analysis (GSK study identifier: 117102).
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