simulationProfile

A simulation profile for generating synthetic patient level prediction data

datasets

A user friendly way to create patient level prediction models using
the Observational Medical Outcomes Partnership Common Data Model. Given a cohort
of interest and an outcome of interest, the package can use data in the Common
Data Model to build a large set of features. These features can then be used to
fit a predictive model with a number of machine learning algorithms. This is
further described in Reps (2017) <doi:10.1093/jamia/ocy032>.

Egill Fridgeirsson

PatientLevelPrediction

Develop Clinical Prediction Models Using the Common Data Model

Jenna Reps

Martijn Schuemie

Marc Suchard

Patrick Ryan

Peter Rijnbeek

Observational Health Data Science and Informatics 

simulationProfile function

A data frame containing the following elements:<dl>
<dt>covariatePrevalence</dt>
<dd>prevalence of all covariates</dd><dt>outcomeModels</dt>
<dd>regression model parameters to simulate outcomes</dd><dt>metaData</dt>
<dd>settings used to simulate the profile</dd><dt>covariateRef</dt>
<dd>covariateIds and covariateNames</dd><dt>timePrevalence</dt>
<dd>time window</dd><dt>exclusionPrevalence</dt>
<dd>prevalence of exclusion of covariates</dd>
</dl>

Format

A simulation profile for generating synthetic patient level prediction data — simulationProfile

A data frame containing the following elements:<dl>
<dt>covariatePrevalence</dt>
<dd>prevalence of all covariates</dd>

<dt>outcomeModels</dt>
<dd>regression model parameters to simulate outcomes</dd>

<dt>metaData</dt>
<dd>settings used to simulate the profile</dd>

<dt>covariateRef</dt>
<dd>covariateIds and covariateNames</dd>

<dt>timePrevalence</dt>
<dd>time window</dd>

<dt>exclusionPrevalence</dt>
<dd>prevalence of exclusion of covariates</dd>


</dl>

simulationProfile: A simulation profile for generating synthetic patient level prediction data

Description

Usage

Arguments

Format