permute

Sets the marginal treatment effect to zero and then permute all treatment
indicators.

Implements methods to fit Virtual Twins models (Foster et al.
(2011) <doi:10.1002/sim.4322>) for identifying subgroups with differential
effects in the context of clinical trials while controlling the probability
of falsely detecting a differential effect when the conditional average
treatment effect is uniform across the study population using parameter
selection methods proposed in Wolf et al. (2022)
<doi:10.1177/17407745221095855>.

Jack Wolf

tehtuner

Fit and Tune Models to Detect Treatment Effect Heterogeneity

permute function

<dl><dt>data</dt>
<dd>a data frame containing a response, binary treatment indicators,
and covariates.</dd>
<dt>Trt</dt>
<dd>a string specifying the name of the column of <code>data</code>
contains the treatment indicators.</dd>
<dt>Y</dt>
<dd>a string specifying the name of the column of <code>data</code>
contains the response.</dd>
<dt>zbar</dt>
<dd>the estimated marginal treatment effect</dd></dl>

Arguments

Generate a dataset with permuted treatment indicators — permute

<dl>

<dt>data</dt>
<dd>a data frame containing a response, binary treatment indicators,
and covariates.</dd>


<dt>Trt</dt>
<dd>a string specifying the name of the column of <code>data</code>
contains the treatment indicators.</dd>


<dt>Y</dt>
<dd>a string specifying the name of the column of <code>data</code>
contains the response.</dd>


<dt>zbar</dt>
<dd>the estimated marginal treatment effect</dd>

</dl>

permute: Generate a dataset with permuted treatment indicators

Description

Usage

Value

Arguments