simDataFullyObs

A simulated observational study data frame with no missing data.

datasets

Implements the G-Formula method for causal inference with time-varying treatments and
confounders using Bayesian multiple imputation methods, as described by
Bartlett, Olarte Parra and Daniel (2023) <arXiv:2301.12026>. It creates multiple synthetic imputed
datasets under treatment regimes of interest using the 'mice' package. These can then be analysed
using rules developed for analysing multiple synthetic datasets.

Jonathan Bartlett

gFormulaMI

G-Formula for Causal Inference via Multiple Imputation

Camila Olarte Parra

Erik van Zwet

simDataFullyObs function

<div class="section" id="simdatafullyobs">
<h3><code>simDataFullyObs</code></h3>
A data frame with 5000 rows and the following variables:<dl>
<dt>l0</dt>
<dd>Continuous baseline confounder</dd><dt>a0</dt>
<dd>Binary baseline treatment</dd><dt>l1</dt>
<dd>Continuous confounder at time 1</dd><dt>a1</dt>
<dd>Binary treatment at time 1</dd><dt>l2</dt>
<dd>Continuous confounder at time 2</dd><dt>a2</dt>
<dd>Binary treatment at time 2</dd><dt>y</dt>
<dd>Continuous final outcome</dd>...</dl></div>

Format

Simulated fully observed data frame — simDataFullyObs

<div class='section' id='simdatafullyobs'>
<h3><code>simDataFullyObs</code></h3>


A data frame with 5000 rows and the following variables:<dl>
<dt>l0</dt>
<dd>Continuous baseline confounder</dd>

<dt>a0</dt>
<dd>Binary baseline treatment</dd>

<dt>l1</dt>
<dd>Continuous confounder at time 1</dd>

<dt>a1</dt>
<dd>Binary treatment at time 1</dd>

<dt>l2</dt>
<dd>Continuous confounder at time 2</dd>

<dt>a2</dt>
<dd>Binary treatment at time 2</dd>

<dt>y</dt>
<dd>Continuous final outcome</dd>

...

</dl>

</div>

simDataFullyObs: Simulated fully observed data frame

Description

Usage

Arguments

Format

`simDataFullyObs`