StratifiedMedicine (version 0.1.3)

ple_bart: Patient-level Estimates: BART

Description

Uses the BART algorithm (Chipman et al 2010; BART R package) to obtain patient-level estimates. Used for continuous or binary outcomes. Covariate by treatment interactions are automatically included in BART model (as in Hahn et al 2017).

Usage

ple_bart(Y, A, X, Xtest, family = "gaussian", ...)

Arguments

Y

The outcome variable. Must be numeric or survival (ex; Surv(time,cens) )

A

Treatment variable. (a=1,...A)

X

Covariate space.

Xtest

Test set

family

Outcome type ("gaussian", "binomial"), default is "gaussian"

...

Any additional parameters, not currently passed through.

Value

Trained BART model(s) and patient-level estimates (E(Y|X,1), E(Y|X,0), E(Y|X,1)-E(Y|X,0)) for train/test sets.

• mods - trained model(s)

• mu_train - Patient-level estimates (training set)

• mu_test - Patient-level estimates (test set)

Examples

# NOT RUN {
library(StratifiedMedicine)

## Continuous ##
dat_ctns = generate_subgrp_data(family="gaussian")
Y = dat_ctns$Y
X = dat_ctns$X
A = dat_ctns$A
train = data.frame(Y, A, X)

# BART #
# }
# NOT RUN {
require(BART)
mod1 = ple_bart(Y, A, X, Xtest=X)
summary(mod1$mu_train)
summary(predict(mod1, newdata=X))
# }
# NOT RUN {

# }