Surrogate (version 1.7)

Restrictions.BinBin: Examine restrictions in \(\bold{\pi}_{f}\) under different montonicity assumptions for binary \(S\) and \(T\)

Description

The function Restrictions.BinBin gives an overview of the restrictions in \(\bold{\pi}_{f}\) under different assumptions regarding montonicity when both \(S\) and \(T\) are binary.

Usage

Restrictions.BinBin(pi1_1_, pi1_0_, pi_1_1, pi_1_0, pi0_1_, pi_0_1)

Arguments

pi1_1_

A scalar that contains \(P(T=1,S=1|Z=0)\), i.e., the proability that \(S=T=1\) when under treatment \(Z=0\).

pi1_0_

A scalar that contains \(P(T=1,S=0|Z=0)\).

pi_1_1

A scalar that contains \(P(T=1,S=1|Z=1)\).

pi_1_0

A scalar that contains \(P(T=1,S=0|Z=1)\).

pi0_1_

A scalar that contains \(P(T=0,S=1|Z=0)\).

pi_0_1

A scalar that contains \(P(T=0,S=1|Z=1)\).

Value

An overview of the restrictions for the freely varying parameters imposed by the data is provided

References

Alonso, A., Van der Elst, W., & Molenberghs, G. (2014). Validation of surrogate endpoints: the binary-binary setting from a causal inference perspective.

See Also

MarginalProbs

Examples

# NOT RUN {
Restrictions.BinBin(pi1_1_=0.262, pi0_1_=0.135, pi1_0_=0.286, 
pi_1_1=0.637, pi_1_0=0.078, pi_0_1=0.127)
# }

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