generate_subgrp_data

Simulation/real data-sets; useful for testing new models and PRISM configurations.

A toolkit for stratified medicine, subgroup identification, and precision medicine.
Current tools include (1) filtering models (reduce covariate space), (2) patient-level estimate
models (counterfactual patient-level quantities, such as the conditional average treatment effect),
(3) subgroup identification models (find subsets of patients with similar treatment effects),
and (4) treatment effect estimation and inference (for the overall population and discovered
subgroups). These tools can be customized and are directly used in PRISM
(patient response identifiers for stratified medicine; Jemielita and Mehrotra 2019
<doi:10.48550/arXiv.1912.03337>).

Thomas Jemielita

StratifiedMedicine

Stratified Medicine

generate_subgrp_data function

<dl><dt>n</dt>
<dd>sample size (default=800)</dd>
<dt>seed</dt>
<dd>seed number (default=513413)</dd>
<dt>family</dt>
<dd>Outcome type ("gaussian", "binomial", "survival")</dd>
<dt>null</dt>
<dd>Simulate null hypothesis of no treatment effect and no subgruops. Default
is FALSE.</dd>
<dt>...</dt>
<dd>Any additional parameters, not currently passed through.</dd></dl>

Arguments

Generate Subgroup Data-sets — generate_subgrp_data

<dl>

<dt>n</dt>
<dd>sample size (default=800)</dd>


<dt>seed</dt>
<dd>seed number (default=513413)</dd>


<dt>family</dt>
<dd>Outcome type ("gaussian", "binomial", "survival")</dd>


<dt>null</dt>
<dd>Simulate null hypothesis of no treatment effect and no subgruops. Default
is FALSE.</dd>


<dt>...</dt>
<dd>Any additional parameters, not currently passed through.</dd>

</dl>

generate_subgrp_data: Generate Subgroup Data-sets

Description

Usage

Value

Arguments