ICA_alpha_ContCont: Assess surrogacy using a Rényi divergence based family of...

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

Assess surrogacy using a Rényi divergence based family of metrics in the causal-inference single-trial setting in normal case

Description

The function ICA_alpha_ContCont() is a set of metrics to evaluate surrogacy. ICA_alpha have the similar mathematical properties with ICA.ContCont().

Usage

ICA_alpha_ContCont(
  alpha = numeric(),
  T0S0,
  T1S1,
  T0T0 = 1,
  T1T1 = 1,
  S0S0 = 1,
  S1S1 = 1,
  T0T1 = seq(-1, 1, by = 0.1),
  T0S1 = seq(-1, 1, by = 0.1),
  T1S0 = seq(-1, 1, by = 0.1),
  S0S1 = seq(-1, 1, by = 0.1)
)

Arguments

alpha	(numeric) is order ⁠alpha in [0, infinity]⁠
T0S0	A scalar or vector that specifies the correlation(s) between the surrogate and the true endpoint in the control treatment condition
T1S1	A scalar or vector that specifies the correlation(s) between the surrogate and the true endpoint in the control treatment condition
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 control treatment condition
S0S0	A scalar that specifies the variance of the true endpoint in the control treatment condition
S1S1	A scalar that specifies the variance of the true endpoint in the control treatment condition
T0T1	A scalar or vector that contains the correlation(s) between the counterfactuals T0 and T1
T0S1	A scalar or vector that contains the correlation(s) between the counterfactuals T0 and S1
T1S0	A scalar or vector that contains the correlation(s) between the counterfactuals T1 and S0
S0S1	A scalar or vector that contains the correlation(s) between the counterfactuals S0 and S1

Value

• 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_{\Delta} values.

• rho: A scalar or vector that contains the individual causal association \rho_{\Delta}

• ICA: A scalar or vector that contains the individual causal association \rho_{\Delta}^2=ICA

• ICA_alpha: A scalar or vector that contains the individual causal association ICA_{\alpha}

• Sigmas: A data.frame that contains the \sigma_{\Delta T} and \sigma_{\Delta S}

Surrogate documentation built on April 11, 2025, 6:09 p.m.