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Table 1 True average treatment effects θ j used to generate networks of trials

From: A test for reporting bias in trial networks: simulation and case studies

Treatment Dispersion of No. of True average treatment effects
effectψ treatment effectν meta-analysesJ θ j ,j=1,,J
ψ1= log(0.75) ν 1=0.02 6 0.793, 0.889, 0.741, 0.954, 0.569, 0.684
ψ1= log(0.75) ν 2=0.08 6 0.808, 0.725, 0.876, 0.698, 0.699, 0.395
ψ2= log(0.95) ν 1=0.02 6 0.796, 1.172, 1.000, 1.171, 1.099, 0.883
ψ2= log(0.95) ν 2=0.08 6 0.491, 1.214, 0.936, 0.977, 1.451, 0.754
ψ1= log(0.75) ν 1=0.02 10 0.658, 0.852, 0.696, 0.889, 0.741, 0.722, 0.645, 0.683, 0.816, 0.796
ψ1= log(0.75) ν 2=0.08 10 0.978, 0.432, 1.149, 0.706, 0.432, 0.751, 0.679, 0.653, 0.624, 0.568
ψ2= log(0.95) ν 1=0.02 10 1.081, 1.089, 0.767, 0.973, 0.781, 0.763, 1.266, 0.816, 1.066, 0.992
ψ2= log(0.95) ν 2=0.08 10 0.845, 0.637, 1.030, 0.799, 0.541, 0.851, 1.063, 1.307, 0.674, 0.732
  1. We set the relative effects θ j ,j=1,…,J from log( θ j )N(ψ,ν).