Bootstrap.MEP.BinBin: 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.

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.

References

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

Examples

Run this code

# 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)
# }

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