Sim.Data.CounterfactualsBinBin: Simulate a dataset that contains counterfactuals for binary...
In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

View source: R/SimDataCounterfactualsBinBin.R

Sim.Data.CounterfactualsBinBin

R Documentation

Simulate a dataset that contains counterfactuals for binary endpoints

Description

The function Sim.Data.CounterfactualsBinBin simulates a dataset that contains four (binary) counterfactuals (i.e., potential outcomes) and a (binary) treatment indicator. The counterfactuals T_0 and T_1 denote the true endpoints of a patient under the control and the experimental treatments, respectively, and the counterfactuals S_0 and S_1 denote the surrogate endpoints of the patient under the control and the experimental treatments, respectively. The user can specify the number of patients and the desired probabilities of the vector of potential outcomes (i.e., \bold{{Y'}_c}=(T_0, T_1, S_0, S_1)).

Usage

Sim.Data.CounterfactualsBinBin(Pi_s=rep(1/16, 16), 
N.Total=2000, Seed=sample(1:1000, size=1))

Arguments

`Pi_s`	The vector of probabilities of the potential outcomes, i.e., `pi_{0000}`, `pi_{0100}`, `pi_{0010}`, `pi_{0001}`, `pi_{0101}`, `pi_{1000}`, `pi_{1010}`, `pi_{1001}`, `pi_{1110}`, `pi_{1101}`, `pi_{1011}`, `pi_{1111}`, `pi_{0110}`, `pi_{0011}`, `pi_{0111}`, `pi_{1100}`. Default `rep(1/16, 16)`.
`N.Total`	The desired number of patients in the simulated dataset. Default `2000`.
`Seed`	A seed that is used to generate the dataset. Default `sample(x=1:1000, size=1)`, i.e., a random number between 1 and 1000.

Details

The generated object Data.STSBinBin.Counter (which contains the counterfactuals) and Data.STSBinBin.Obs (the "observable data") (of class data.frame) is placed in the workspace.

Value

An object of class Sim.Data.CounterfactualsBinBin with components,

`Data.STSBinBin.Obs`	The generated dataset that contains the "observed" surrogate endrpoint, true endpoint, and assigned treatment.
`Data.STSBinBin.Counter`	The generated dataset that contains the counterfactuals.
`Vector_Pi`	The vector of probabilities of the potential outcomes, i.e., `pi_{0000}`, `pi_{0100}`, `pi_{0010}`, `pi_{0001}`, `pi_{0101}`, `pi_{1000}`, `pi_{1010}`, `pi_{1001}`, `pi_{1110}`, `pi_{1101}`, `pi_{1011}`, `pi_{1111}`, `pi_{0110}`, `pi_{0011}`, `pi_{0111}`, `pi_{1100}`.
`Pi_Marginals`	The vector of marginal probabilities `\pi_{1 \cdot 1 \cdot}`, `\pi_{0 \cdot 1 \cdot}`, `\pi_{1 \cdot 0 \cdot}`, `\pi_{0 \cdot 0 \cdot}`, `\pi_{\cdot 1 \cdot 1}`, `\pi_{\cdot 1 \cdot 0}`, `\pi_{\cdot 0 \cdot 1}`, `\pi_{\cdot 0 \cdot 0}`.
`True.R2_H`	The true `R_H^2` value.
`True.Theta_T`	The true odds ratio for `T`.
`True.Theta_S`	The true odds ratio for `S`.

Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

Examples

## Generate a dataset with 2000 patients, and values 1/16
## for all proabilities between the counterfactuals:
Sim.Data.CounterfactualsBinBin(N.Total=2000)

Surrogate documentation built on June 22, 2024, 9:16 a.m.