Bootstrap.MEP.BinBin {Surrogate}

Bootstrap 95% CI around the maximum-entropy ICA and SPF (surrogate predictive function)

Description

Computes a 95% bootstrap-based CI around the maximum-entropy ICA and SPF (surrogate predictive function) in the binary-binary setting

Usage

Bootstrap.MEP.BinBin(Data, Surr, True, Treat, M=100, Seed=123)

Arguments

Data

The dataset to be used.

Surr

The name of the surrogate variable.

True

The name of the true endpoint.

Treat

The name of the treatment indicator.

M

The number of bootstrap samples taken. Default M=1000.

Seed

The seed to be used. Default Seed=123.

Value

R2H

The vector the bootstrapped MEP ICA values.

r_1_1

The vector of the bootstrapped bootstrapped MEP r(1, 1) values.

r_min1_1

The vector of the bootstrapped bootstrapped MEP r(-1, 1).

r_0_1

The vector of the bootstrapped bootstrapped MEP r(0, 1).

r_1_0

The vector of the bootstrapped bootstrapped MEP r(1, 0).

r_min1_0

The vector of the bootstrapped bootstrapped MEP r(-1, 0).

r_0_0

The vector of the bootstrapped bootstrapped MEP r(0, 0).

r_1_min1

The vector of the bootstrapped bootstrapped MEP r(1, -1).

r_min1_min1

The vector of the bootstrapped bootstrapped MEP r(-1, -1).

r_0_min1

The vector of the bootstrapped bootstrapped MEP r(0, -1).

vector_p

The matrix that contains all bootstrapped maximum entropy distributions of the vector of the potential outcomes.

Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

Alonso, A., & Van der Elst, W. (2015). A maximum-entropy approach for the evluation of surrogate endpoints based on causal inference.

See Also

ICA.BinBin, ICA.BinBin.Grid.Sample, ICA.BinBin.Grid.Full, plot MaxEntSPF BinBin

Examples

## Not run:  # time consuming code part
MEP_CI <- Bootstrap.MEP.BinBin(Data = Schizo_Bin, Surr = "BPRS_Bin", True = "PANSS_Bin",
                     Treat = "Treat", M = 500, Seed=123)
summary(MEP_CI)

## End(Not run)