TrialLevelMA: Estimates trial-level surrogacy in the meta-analytic...
In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

TrialLevelMA

R Documentation

Estimates trial-level surrogacy in the meta-analytic framework

Description

The function TrialLevelMA estimates trial-level surrogacy based on the vectors of treatment effects on S (i.e., \alpha_{i}) and T (i.e., \beta_{i}) in the different trials. In particular, \beta_{i} is regressed on \alpha_{i} and the classical coefficient of determination of the fitted model provides an estimate of R^2_{trial}. In addition, the standard error and CI are provided.

Usage

TrialLevelMA(Alpha.Vector, Beta.Vector, 
N.Vector, Weighted=TRUE, Alpha=.05)

Arguments

`Alpha.Vector`	The vector of treatment effects on `S` in the different trials, i.e., `\alpha_{i}`.
`Beta.Vector`	The vector of treatment effects on `T` in the different trials, i.e., `\beta_{i}`.
`N.Vector`	The vector of trial sizes `N_{i}`.
`Weighted`	Logical. If `TRUE`, then a weighted regression analysis is conducted. If `FALSE`, then an unweighted regression analysis is conducted. Default `TRUE`.
`Alpha`	The `\alpha`-level that is used to determine the confidence intervals around `R^2_{trial}` and `R_{trial}`. Default `0.05`.

Value

An object of class TrialLevelMA with components,

`Alpha.Vector`	The vector of treatment effects on `S` in the different trials.
`Beta.Vector`	The vector of treatment effects on `T` in the different trials.
`N.Vector`	The vector of trial sizes `N_{i}`.
`Trial.R2`	A `data.frame` that contains the trial-level coefficient of determination (`R^2_{trial}`), its standard error and confidence interval.
`Trial.R`	A `data.frame` that contains the trial-level correlation coefficient (`R_{trial}`), its standard error and confidence interval.
`Model.2.Fit`	The fitted stage `2` model.

Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

Burzykowski, T., Molenberghs, G., & Buyse, M. (2005). The evaluation of surrogate endpoints. New York: Springer-Verlag.

Buyse, M., Molenberghs, G., Burzykowski, T., Renard, D., & Geys, H. (2000). The validation of surrogate endpoints in meta-analysis of randomized experiments. Biostatistics, 1, 49-67.

Examples

# Generate vector treatment effects on S
set.seed(seed = 1)
Alpha.Vector <- seq(from = 5, to = 10, by=.1) + runif(min = -.5, max = .5, n = 51)
# Generate vector treatment effects on T
set.seed(seed=2)
Beta.Vector <- (Alpha.Vector * 3) + runif(min = -5, max = 5, n = 51)
# Vector of sample sizes of the trials (here, all n_i=10)
N.Vector <- rep(10, times=51)

# Apply the function to estimate R^2_{trial}
Fit <- TrialLevelMA(Alpha.Vector=Alpha.Vector,
Beta.Vector=Beta.Vector, N.Vector=N.Vector)

# Plot the results and obtain summary
plot(Fit)
summary(Fit)

Surrogate documentation built on Sept. 25, 2023, 5:07 p.m.