solvd.sub

Dataset for use in beanz examples and vignettes.

It is vital to assess the heterogeneity of treatment effects
(HTE) when making health care decisions for an individual patient or a group
of patients. Nevertheless, it remains challenging to evaluate HTE based
on information collected from clinical studies that are often designed and
conducted to evaluate the efficacy of a treatment for the overall population.
The Bayesian framework offers a principled and flexible approach to estimate
and compare treatment effects across subgroups of patients defined by their
characteristics. This package allows users to explore a wide range of Bayesian
HTE analysis models, and produce posterior inferences about HTE. See Wang et al.
(2018) <DOI:10.18637/jss.v085.i07> for further details.

Chenguang Wang

beanz

Bayesian Analysis of Heterogeneous Treatment Effect

solvd.sub function

A dataframe with 6 variables:<dl>
 <dt>trt</dt>
<dd>treatment assignment</dd> <dt>y</dt>
<dd>time to death or first hospitalization</dd> <dt>censor</dt>
<dd>censoring status</dd> <dt>sodium</dt>
<dd>level of sodium</dd> <dt>lvef</dt>
<dd>level of lvef</dd> <dt>any.vasodilator.use</dt>
<dd>level of use of vasodilator</dd>
</dl>

Format

Subject level data from SOLVD trial — solvd.sub

A dataframe with 6 variables:<dl>
 <dt>trt</dt>
<dd>treatment assignment</dd>

 <dt>y</dt>
<dd>time to death or first hospitalization</dd>

 <dt>censor</dt>
<dd>censoring status</dd>

 <dt>sodium</dt>
<dd>level of sodium</dd>

 <dt>lvef</dt>
<dd>level of lvef</dd>

 <dt>any.vasodilator.use</dt>
<dd>level of use of vasodilator</dd>


</dl>

solvd.sub: Subject level data from SOLVD trial

Description

Arguments

Format

Details

References