computeTimeStability

Check stability of outcome rate over time

Execute the self-controlled case series (SCCS) design using observational
data in the OMOP Common Data Model. Extracts all necessary data from the database and
transforms it to the format required for SCCS. Age and season can be modeled
using splines assuming constant hazard within calendar months. Event-dependent
censoring of the observation period can be corrected for. Many exposures can be
included at once (MSCCS), with regularization on all coefficients except for the
exposure of interest. Includes diagnostics for all major assumptions of the SCCS.

Martijn Schuemie

SelfControlledCaseSeries

Self-Controlled Case Series

Patrick Ryan

Trevor Shaddox

Marc Suchard

computeTimeStability function

<dl><dt>studyPopulation</dt>
<dd>An object created using the <code>createStudyPopulation()</code> function.</dd>
<dt>sccsModel</dt>
<dd>Optional: A fitted SCCS model as created using <code>fitSccsModel()</code>. If the
model contains splines for seasonality and or calendar time these will be adjusted
for before computing stability.</dd>
<dt>maxRatio</dt>
<dd>The maximum global ratio between the observed and expected count.</dd>
<dt>alpha</dt>
<dd>The alpha (type 1 error) used to test for stability.</dd></dl>

Arguments

Check stability of outcome rate over time — computeTimeStability

<dl>

<dt>studyPopulation</dt>
<dd>An object created using the <code>createStudyPopulation()</code> function.</dd>


<dt>sccsModel</dt>
<dd>Optional: A fitted SCCS model as created using <code>fitSccsModel()</code>. If the
model contains splines for seasonality and or calendar time these will be adjusted
for before computing stability.</dd>


<dt>maxRatio</dt>
<dd>The maximum global ratio between the observed and expected count.</dd>


<dt>alpha</dt>
<dd>The alpha (type 1 error) used to test for stability.</dd>

</dl>

computeTimeStability: Check stability of outcome rate over time

Description

Usage

Value

Arguments

Details