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Analyzing Ten Hypothetical TrialsSuppose that 10 estimates for the treatment effects on S and T are available in the published literature, as well as the sample sizes on which these estimates were based. Using this information, an estimate of R_{rial} can be obtained using the following command: # Fit the model > Trial_Fit < TrialLevelMA( Alpha.Vector=c(4.7, 4.9, 5.2, 5.7, 5.1, 5.8, 6.0, 5.8, 5.9,
The fitted object Trial_Fit of class TrialLevelMA can subsequently be examined by applying the summary() and plot() functions: # Obtain summary of the results: > summary(Trial_Fit) Function call: TrialLevelMA(Alpha.Vector = c(4.7, 4.9, 5.2, 5.7, 5.1, 5.8, 6, 5.8, 5.9, 5.4), Beta.Vector = c(13.6, 15.3, 15.9, 16.4, 16.1,18.5, 17.3, 18.2, 17.7, 16.4), N.Vector = c(130, 140, 150, 200, 210, 240, 300, 350, 350, 400)) # Data summary and descriptives Total number of trials: 10 # Metaanalytic results summary R2 Trial Standard Error CI lower limit CI upper limit 0.7608 0.1577 0.4517 1.0000 R Trial Standard Error CI lower limit CI upper limit 0.8722 0.1729 0.5382 0.9695 # Obtain plot of the (triallevel) results > plot(Trial_Fit) # Generated output: The output shows that the treatment effect on T can be predicted with moderate accuracy based on the treatment effect on S, i.e., R_{rial} = 07608 with 95% confidence interval [0.4517; 1.000]. Notice that the confidence interval around i?2_{rial} is wide, which could be expected given the small number of clustering units (trials) that were available for analysis. 
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