estimate_ICA_BinCont: Estimate ICA in Binary-Continuous Setting

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

Estimate ICA in Binary-Continuous Setting

Description

estimate_ICA_BinCont() estimates the individual causal association (ICA) for a sample of individual causal treatment effects with a continuous surrogate and a binary true endpoint. The ICA in this setting is defined as follows,

R^2_H = \frac{I(\Delta S; \Delta T)}{H(\Delta T)}

where I(\Delta S; \Delta T) is the mutual information and H(\Delta T) the entropy.

Usage

estimate_ICA_BinCont(delta_S, delta_T)

Arguments

delta_S	(numeric) Vector of individual causal treatment effects on the surrogate.
delta_T	(integer) Vector of individual causal treatment effects on the true endpoint. Should take on one of the following values: -1L, 0L, or 1L.

Value

(numeric) Estimated ICA

Surrogate documentation built on Sept. 25, 2023, 5:07 p.m.