Surrogate (version 3.4.1)

ICA.Sample.ControlTreat: 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)

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 ${\rho }_{\mathrm{\Delta }}$ values.

ICA

A scalar or vector that contains the individual causal association (ICA; ${\rho }_{\mathrm{\Delta }}$) value(s).

GoodSurr

A data.frame that contains the ICA (${\rho }_{\mathrm{\Delta }}$), ${\sigma }_{{\mathrm{\Delta }}_T}$, and $\delta$.

Variances

A data.frame that contains the variances for S and T in both treatment conditions.

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 ${\rho }_{\mathrm{\Delta }}$.

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 ${\rho }_{\mathrm{\Delta }}$.

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 ${\rho }_{\mathrm{\Delta }}$. 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 ${\rho }_{\mathrm{\Delta }}$. 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 ${\rho }_{\mathrm{\Delta }}$. 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 ${\rho }_{\mathrm{\Delta }}$. 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 ${\rho }_{\mathrm{\Delta }}$. 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 ${\rho }_{\mathrm{\Delta }}$. 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 ${\rho }_{\mathrm{\Delta }}$. 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 ${\rho }_{\mathrm{\Delta }}$. 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.

Author

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.

See Also

MICA.ContCont, ICA.ContCont, Single.Trial.RE.AA, plot Causal-Inference ContCont

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)