pmmFit

Predictive mean matching using lasso

internal

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 

pmmFit function

<dl><dt>data</dt>
<dd>An andromeda object with the following fields:
xObs: covariates table for observed data
xMiss: covariates table for missing data
yObs: outcome variable that we want to impute</dd>
<dt>k</dt>
<dd>The number of donors to use for matching (default 5)</dd>
<dt>alpha</dt>
<dd>Elastic-net mixing parameter (<code>1</code> = lasso, <code>0</code> = ridge)</dd></dl>

Arguments

Predictive mean matching using lasso — pmmFit

<dl>

<dt>data</dt>
<dd>An andromeda object with the following fields:
xObs: covariates table for observed data
xMiss: covariates table for missing data
yObs: outcome variable that we want to impute</dd>


<dt>k</dt>
<dd>The number of donors to use for matching (default 5)</dd>


<dt>alpha</dt>
<dd>Elastic-net mixing parameter (<code>1</code> = lasso, <code>0</code> = ridge)</dd>

</dl>

pmmFit: Predictive mean matching using lasso

Description

Usage

Arguments