plpDataSimulationProfile

datasets

A package for creating patient level prediction models. Given a
cohort of interest and an outcome of interest, the package can use data in the
OMOP Common Data Model to build a large set of features. These features can then
be assessed to fit a predictive model using a number of machine learning algorithms.
Several performance measures are implemented for model evaluation.

Jenna Reps

PatientLevelPrediction

Package for patient level prediction using data in the OMOP Common Data
Model

Martijn Schuemie

Marc Suchard

Patrick Ryan

Peter Rijnbeek

plpDataSimulationProfile function

A data frame containing the following elements:<dl class="dl-horizontal">
 <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 — plpDataSimulationProfile

A data frame containing the following elements:<dl class='dl-horizontal'>
 <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>

A simulation profile

plpDataSimulationProfile: A simulation profile

Description

Usage

Arguments

Format