So (2) τ 2 = ϵ 2 I 2 1 − I 2 where ϵ 2 = 1 k ∑ 2 σ 2 n i, with n i the groupsize of trial i (i = 1…k) and σ the standard deviation of the outcome variable of the trials.
A carefully prepared subgroup analysis with predefined baseline variables of the aforementioned trial showed that early surgery compared to prolonged conservative care yielded a significant faster rate of recovery in patients with leg pain provoked by sitting [ 35].
x1(i) = level of first predictor variable of the ith trial, x2(i) = level of second predictor variable in the ith trial, and x3(i) and x4(i) = levels for w and p, respectively.
Consistent with other strands of the evaluation [ 19], for the purpose of calculating variables, the trial start date was taken as the date of telehealth installation for intervention patients, and as the date of the initial project team visit for controls.
Averages of the output variables of the four individual trials were used for comparison and statistical evaluation.
Information for all variables was complete for the remaining 191 records except for the variable: "Year of the start of the trial".
2. Comparison of the group at the beginning of the trial for response variables, descriptive variables, and prediction factors.
Another source of variability is the different mixture of various prognostic variables between the trials.
The independent variables of the task switching analyses were trial type (repetition, switch), block type (PRB, MB), CTI (100 ms, 1000 ms) and stimulus valence (univalent, bivalent).
We adjusted for the natural increase in HbA1c over time, ageing of patients, and the age-related increase in complication risk, accounting for uncertainties by including distributions in values of input variables, including HbA1c at the end of the trial and mortality risk (10, 31).
