data_in

A simulated dataset from the 2/1 partially nested design with treatment-incuded clustering

datasets

Implements the methods for assessing heterogeneous cluster-specific treatment effects in partially nested designs as described in Liu (2024) <doi:10.1037/met0000723>. The estimation uses the multiply robust method, allowing for the use of machine learning methods in model estimation (e.g., random forest, neural network, and the super learner ensemble). Partially nested designs (also known as partially clustered designs) are designs where individuals in the treatment arm are assigned to clusters (e.g., teachers, tutoring groups, therapists), whereas individuals in the control arm have no such clustering.

Xiao Liu

PND.heter.cluster

Estimating the Cluster Specific Treatment Effects in Partially
Nested Designs

data_in function

A data frame with 400 rows and 8 variables:<dl>
 <dt>Y</dt>
<dd>Outcome.</dd> <dt>K</dt>
<dd>Cluster assignment in the treatment arm.</dd> <dt>tt</dt>
<dd>Treatment assignment. 1 for individuals assigned to the treatment arm. 0 for individuals assigned to the control arm. The control arm is unclustered.</dd> <dt>X_dat.1</dt>
<dd>Baseline covariates.</dd> <dt>X_dat.2</dt>
<dd>Baseline covariates.</dd> <dt>X_dat.3</dt>
<dd>Baseline covariates.</dd> <dt>X_dat.4</dt>
<dd>Baseline covariates.</dd> <dt>id</dt>
<dd>Individual id.</dd>
</dl>

Format

data_in — data_in

A data frame with 400 rows and 8 variables:<dl>
 <dt>Y</dt>
<dd>Outcome.</dd>

 <dt>K</dt>
<dd>Cluster assignment in the treatment arm.</dd>

 <dt>tt</dt>
<dd>Treatment assignment. 1 for individuals assigned to the treatment arm. 0 for individuals assigned to the control arm. The control arm is unclustered.</dd>

 <dt>X_dat.1</dt>
<dd>Baseline covariates.</dd>

 <dt>X_dat.2</dt>
<dd>Baseline covariates.</dd>

 <dt>X_dat.3</dt>
<dd>Baseline covariates.</dd>

 <dt>X_dat.4</dt>
<dd>Baseline covariates.</dd>

 <dt>id</dt>
<dd>Individual id.</dd>


</dl>

data_in: data_in

Description

Usage

Arguments

Format