call_dashboard

Visualizing validation results according to four steps, namely, set-selection, estimation, diagnosis, and validation

Validates estimates of (conditional) average treatment effects obtained using observational data by a) making it easy to obtain and visualize estimates derived using a large variety of methods (G-computation, inverse propensity score weighting, etc.), and b) ensuring that estimates are easily compared to a gold standard (i.e., estimates derived from randomized controlled trials). 'RCTrep' offers a generic protocol for treatment effect validation based on four simple steps, namely, set-selection, estimation, diagnosis, and validation. 'RCTrep' provides a simple dashboard to review the obtained results. The validation approach is introduced by Shen, L., Geleijnse, G. and Kaptein, M. (2023) <doi:10.21203/rs.3.rs-2559287/v2>.

Lingjie Shen

RCTrep

Validation of Estimates of Treatment Effects in Observational
Data

Gijs Geleijnse

Maurits Kaptein

call_dashboard function

<dl><dt>source.obj</dt>
<dd>an instantiated object of class <code>TEstimator</code>. The estimates of conditional average treatment effects are compared to those from <code>target.obj</code>.</dd>
<dt>target.obj</dt>
<dd>an instantiated object of class <code>TEstimator</code>. The estimates of conditional average treatment effects are regarded as unbiased of truth.</dd>
<dt>source.obj.rep</dt>
<dd>an instantiated object of class <code>SEstimator</code>. The estimates of conditional average treatment effects are compared to those from <code>target.obj</code>.</dd></dl>

Arguments

Visualizing validation results according to four steps, namely, set-selection, estimation, diagnosis, and validation — call_dashboard

<dl>

<dt>source.obj</dt>
<dd>an instantiated object of class <code>TEstimator</code>. The estimates of conditional average treatment effects are compared to those from <code>target.obj</code>.</dd>


<dt>target.obj</dt>
<dd>an instantiated object of class <code>TEstimator</code>. The estimates of conditional average treatment effects are regarded as unbiased of truth.</dd>


<dt>source.obj.rep</dt>
<dd>an instantiated object of class <code>SEstimator</code>. The estimates of conditional average treatment effects are compared to those from <code>target.obj</code>.</dd>

</dl>

call_dashboard: Visualizing validation results according to four steps, namely, set-selection, estimation, diagnosis, and validation

Description

Usage

Value

Arguments