Surrogate (version 3.2.6)

MaxEntContCont: Use the maximum-entropy approach to compute ICA in the continuous-continuous sinlge-trial setting

Description

In a surrogate evaluation setting where both $S$ and $T$ are continuous endpoints, a sensitivity-based approach where multiple 'plausible values' for ICA are retained can be used (see functions ICA.ContCont). The function MaxEntContCont identifies the estimate which has the maximuum entropy.

Usage

MaxEntContCont(x, T0T0, T1T1, S0S0, S1S1)

Value

ICA.Max.Ent

The ICA value with maximum entropy.

Max.Ent

The maximum entropy.

Entropy

The vector of entropies corresponding to the vector of 'plausible values' for ICA.

Table.ICA.Entropy

A data.frame that contains the vector of ICA, their entropies, and the correlations between the counterfactuals.

ICA.Fit

The fitted ICA.ContCont object.

Arguments

x

A fitted object of class ICA.ContCont.

T0T0

A scalar that specifies the variance of the true endpoint in the control treatment condition.

T1T1

A scalar that specifies the variance of the true endpoint in the experimental treatment condition.

S0S0

A scalar that specifies the variance of the surrogate endpoint in the control treatment condition.

S1S1

A scalar that specifies the variance of the surrogate endpoint in the experimental treatment condition.

Author

Wim Van der Elst, Ariel Alonso, Paul Meyvisch, & Geert Molenberghs

References

Add

See Also

ICA.ContCont, MaxEntICABinBin

Examples

if (FALSE)  #time-consuming code parts
# Compute ICA for ARMD dataset, using the grid  
# G={-1, -.80, ..., 1} for the undidentifiable correlations

ICA <- ICA.ContCont(T0S0 = 0.769, T1S1 = 0.712, S0S0 = 188.926, 
S1S1 = 132.638, T0T0 = 264.797, T1T1 = 231.771, 
T0T1 = seq(-1, 1, by = 0.2), T0S1 = seq(-1, 1, by = 0.2), 
T1S0 = seq(-1, 1, by = 0.2), S0S1 = seq(-1, 1, by = 0.2))

# Identify the maximum entropy ICA
MaxEnt_ARMD <- MaxEntContCont(x = ICA, S0S0 = 188.926, 
S1S1 = 132.638, T0T0 = 264.797, T1T1 = 231.771)

  # Explore results using summary() and plot() functions
summary(MaxEnt_ARMD)
plot(MaxEnt_ARMD)
plot(MaxEnt_ARMD, Entropy.By.ICA = TRUE)