balance

Checking covariate balance based on estimated cluster assignment probabilities (principal score) and treatment assignment probabilities (propensity score).

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

balance function

<dl><dt>data_in</dt>
<dd>A <code>data.frame</code> containing all necessary variables.</dd>
<dt>atekCl_results</dt>
<dd>[<code>list</code>] 
A list returned from the R function <code>atekCl()</code>.</dd>
<dt>covariate_names</dt>
<dd>[<code>character</code>] 
A character vector of the column names of the baseline covariates for checking balance.</dd>
<dt>ttname</dt>
<dd>[<code>character</code>] 
A character string of the column name of the treatment variable. The treatment variable should be dummy-coded, with 1 for the (clustered) treatment arm and 0 for the (non-clustered) control arm.</dd>
<dt>Kname</dt>
<dd>[<code>character</code>] 
A character string of the column name of the cluster assignment variable. This variable should be coded as 0 for individuals in the control arm, the arm without the cluster assignment.</dd></dl>

Arguments

Checking covariate balance based on estimated cluster assignment probabilities (principal score) and treatment assignment probabilities (propensity score). — balance

<dl>

<dt>data_in</dt>
<dd>A <code>data.frame</code> containing all necessary variables.</dd>


<dt>atekCl_results</dt>
<dd>[<code>list</code>] 
A list returned from the R function <code>atekCl()</code>.</dd>


<dt>covariate_names</dt>
<dd>[<code>character</code>] 
A character vector of the column names of the baseline covariates for checking balance.</dd>


<dt>ttname</dt>
<dd>[<code>character</code>] 
A character string of the column name of the treatment variable. The treatment variable should be dummy-coded, with 1 for the (clustered) treatment arm and 0 for the (non-clustered) control arm.</dd>


<dt>Kname</dt>
<dd>[<code>character</code>] 
A character string of the column name of the cluster assignment variable. This variable should be coded as 0 for individuals in the control arm, the arm without the cluster assignment.</dd>

</dl>

Checking covariate balance based on estimated cluster assignment probabilities (principal score) and treatment assignment probabilities (propensity score).

balance: Checking covariate balance based on estimated cluster assignment probabilities (principal score) and treatment assignment probabilities (propensity score).

Description

Usage

Value

Arguments