# Analyzing Ten Hypothetical Trials

Suppose 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,

- 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))

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

# Meta-analytic 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 (trial-level) 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} = 0-7608 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.