ICA.Sample.ControlTreat: Assess surrogacy in the causal-inference single-trial setting...
In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

View source: R/ICA.Sample.ControlTreat.R

ICA.Sample.ControlTreat

R Documentation

Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) in the Continuous-continuous case using the grid-based sample approach when data is only avalable for the control treatment

Description

The function ICA.Sample.ControlTreat quantifies surrogacy in the single-trial causal-inference framework when data is only avalable for the control treatment.

Usage

ICA.Sample.ControlTreat(T0S0, T1S1=seq(-1, 1, by = 0.001), 
T0T0=1, T1T1=1, S0S0=1, S1S1=1, T0T1=seq(-1, 1, by=.001), 
T0S1=seq(-1, 1, by=.001), T1S0=seq(-1, 1, by=.001), S0S1=seq(-1, 1, by=.001), 
M=50000, M.Target=NA)

Arguments

`T0S0`	A scalar or vector that specifies the correlation(s) between the surrogate and the true endpoint in the control treatment condition that should be considered in the computation of `\rho_{\Delta}`.
`T1S1`	A scalar or vector that specifies the correlation(s) between the surrogate and the true endpoint in the experimental treatment condition that should be considered in the computation of `\rho_{\Delta}`.
`T0T0`	A scalar or vector that specifies the variance of the true endpoint in the control treatment condition that should be considered in the computation of `\rho_{\Delta}`. Default 1.
`T1T1`	A scalar or vector that specifies the variance of the true endpoint in the experimental treatment condition that should be considered in the computation of `\rho_{\Delta}`. Default 1.
`S0S0`	A scalar or vector that specifies the variance of the surrogate endpoint in the control treatment condition that should be considered in the computation of `\rho_{\Delta}`. Default 1.
`S1S1`	A scalar or vector that specifies the variance of the surrogate endpoint in the experimental treatment condition that should be considered in the computation of `\rho_{\Delta}`. Default 1.
`T0T1`	A scalar or vector that contains the correlation(s) between the counterfactuals T0 and T1 that should be considered in the computation of `\rho_{\Delta}`. Default `seq(-1, 1, by=.001)`.
`T0S1`	A scalar or vector that contains the correlation(s) between the counterfactuals T0 and S1 that should be considered in the computation of `\rho_{\Delta}`. Default `seq(-1, 1, by=.001)`.
`T1S0`	A scalar or vector that contains the correlation(s) between the counterfactuals T1 and S0 that should be considered in the computation of `\rho_{\Delta}`. Default `seq(-1, 1, by=.001)`.
`S0S1`	A scalar or vector that contains the correlation(s) between the counterfactuals S0 and S1 that should be considered in the computation of `\rho_{\Delta}`. Default `seq(-1, 1, by=.001)`.
`M`	The number of runs that should be conducted. Default `50000`.
`M.Target`	The number of ICA values that should be identified. Only one argument `M=` or `M.Target=` can be used.

Value

An object of class ICA.ContCont with components,

`Total.Num.Matrices`	An object of class `numeric` that contains the total number of matrices that can be formed as based on the user-specified correlations in the function call.
`Pos.Def`	A `data.frame` that contains the positive definite matrices that can be formed based on the user-specified correlations. These matrices are used to compute the vector of the `\rho_{\Delta}` values.
`ICA`	A scalar or vector that contains the individual causal association (ICA; `\rho_{\Delta}`) value(s).
`GoodSurr`	A `data.frame` that contains the ICA (`\rho_{\Delta}`), `\sigma_{\Delta_{T}}`, and `\delta`.
`Variances`	A `data.frame` that contains the variances for S and T in both treatment conditions.

Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

Van der Elst, W. et al. (submitted). On the Early Identification of Promising Surrogate Endpoints Using Causal Inference.

Examples

# Generate the vector of ICA values when rho_T0S0=.95, 
# sigma_T0T0=90, sigma_T1T1=100,sigma_ S0S0=10, sigma_S1S1=15, and  
# min=-1 max=1 is considered for the correlations
# between the counterfactuals and rho_T1S1:
SurICA2 <- ICA.Sample.ControlTreat(T0S0=.95, T0T0=90, T1T1=100, S0S0=10, 
S1S1=15, M=5000)

# Examine and plot the vector of generated ICA values:
summary(SurICA2)
plot(SurICA2)

Surrogate documentation built on June 8, 2025, 1:09 p.m.