submod_otr: Subgroup Identification: Optimal Treatment Regime (through...

Description Usage Arguments Value References Examples

View source: R/submod_otr.R

Description

For continuous, binary, or survival outcomes, regress I(PLE>thres)~X with weights=abs(PLE) in ctree. For example, PLE could refer to individual treatment effect, E(Y|A=1,X)-E(Y|A=0, X)

Usage

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submod_otr(Y, A, X, Xtest, mu_train, alpha = 0.05,
  minbucket = floor(dim(X)[1] * 0.1), maxdepth = 4, thres = ">0",
  ...)

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

mu_train

Patient-level estimates (See PLE_models)

alpha

Significance level for variable selection (default=0.05)

minbucket

Minimum number of observations in a tree node. Default = floor( dim(train)[1]*0.05 )

maxdepth

Maximum depth of any node in the tree (default=4)

thres

Threshold for PLE, ex: I(PLE>thres). Default is ">0". Direction can be reversed and can include equality sign (ex: "<=")

...

Any additional parameters, not currently passed through.

Value

Trained ctree (optimal treatment regime) model.

References

Zhao et al. (2012) Estimated individualized treatment rules using outcome weighted learning. Journal of the American Statistical Association, 107(409): 1106-1118.

Examples

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library(StratifiedMedicine)

## Continuous ##
dat_ctns = generate_subgrp_data(family="gaussian")
Y = dat_ctns$Y
X = dat_ctns$X
A = dat_ctns$A


## Estimate PLEs (through Ranger) ##
res.ple = ple_model(Y, A, X, Xtest=X, family="gaussian", ple="ple_ranger")

## Fit OTR Subgroup Model ##
res_otr = submod_otr(Y, A, X, Xtest=X, mu_train = res.ple$mu_train)
plot(res_otr$mod)

StratifiedMedicine documentation built on March 1, 2020, 9:07 a.m.