ptehd: Proportion of treatment effect explained by high-dimensional...

In freebird: Estimation and Inference for High Dimensional Mediation and Surrogate Analysis

Proportion of treatment effect explained by high-dimensional surrogates

Description

Estimates the proportion of the treatment effect explained by the indirect effect via high-dimensional surrogates.

Usage

ptehd(Yt, Yc, St, Sc, lambda_range = c(0, 1))

Arguments

Yt	The n-dmensional outcome vector in the treatment group.
Yc	The n-dmensional outcome vector in the control group.
St	The n x p matrix of surrogates in the treatment group.
Sc	The n x p matrix of surrogates in the treatment group.
lambda_range	Min and max of range of range of tuning parameter to use during the constrained l1 optimization step.

Value

A list with components:

est_id	Estimate of indirect effect, defined as \int E(Y | S = s, Z = 1) dF(s | Z = 1) - \int E(Y | S = s, Z = 0) dF(s | Z = 0)
sd_id	Standard deviation of indirect effect estimate
est_total	Estimate of total effect
sd_total	Standard deviation of total effect estimate
V	Covariance matrix of (est_id, est_total)
est_R	Estimate of proportion of treatment effect explained by surrogates
sd_R	Standard deviation of proportion estimate
lambda_used	lambda used during optimization

Author(s)

Ruixuan Zhou

Examples

n = 10
St = replicate(n, rnorm(20, mean = 1))
Sc = replicate(n, rnorm(20))
Yt = 1 + rowSums(St) / 2 + rnorm(n)
Yc = rowSums(Sc) / 3 + rnorm(n)
# Requires installation of mosek to run
## Not run: 
out = ptehd(Yt, Yc, St, Sc)

## End(Not run)

freebird documentation built on Sept. 27, 2022, 5:05 p.m.