ICA_t: The function 'ICA_t()' is to evaluate surrogacy in the...

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

The function ICA_t() is to evaluate surrogacy in the single-trial causal-inference framework.

Description

The function ICA_t() is to evaluate surrogacy in the single-trial causal-inference framework.

Usage

ICA_t(
  df,
  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

df	(numeric) is degree of freedom \nu. The maximum value for df is 342. When df exceeds this threshold, the model behavior aligns with the Individual Causal Association (ICA) under the normal causal model.
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_t: A scalar or vector that contains the individual causal association ICA_{t}

• 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.