consistent-doc

Helper for consistent documentation

internal

Summarises key information in data mapped to the Observational
Medical Outcomes Partnership (OMOP) common data model. Assess suitability to
perform specific epidemiological studies and explore the different domains
to obtain feasibility counts and trends.

Cecilia Campanile

OmopSketch

Characterise Tables of an OMOP Common Data Model Instance

Marta Alcalde-Herraiz

Kim Lopez-Guell

Elin Rowlands

Edward Burn

Martí Català

consistent-doc function

<dl><dt>cdm</dt>
<dd>A <code>cdm_reference</code> object. Use CDMConnector to create a reference
to a database or omock to create a reference to synthetic data.</dd>
<dt>omopTableName</dt>
<dd>A character vector of the names of the tables to
summarise in the cdm object. Run <code>clinicalTables()</code> to check the
available options.</dd>
<dt>ageGroup</dt>
<dd>A list of age groups to stratify the results by. Each element
represents a specific age range. You can give them specific names, e.g.
<code>ageGroup = list(children = c(0, 17), adult = c(18, Inf))</code>.</dd>
<dt>sex</dt>
<dd>Logical; whether to stratify results by sex (<code>TRUE</code>) or not
(<code>FALSE</code>).</dd>
<dt>facet</dt>
<dd>Columns to face by. Formula format can be provided. See possible
columns to face by with: <code>visOmopResults::tidyColumns()</code>.</dd>
<dt>colour</dt>
<dd>Columns to colour by. See possible columns to colour by with:
<code>visOmopResults::tidyColumns()</code>.</dd>
<dt>interval</dt>
<dd>Time interval to stratify by. It can either be "years",
"quarters", "months" or "overall".</dd>
<dt>sample</dt>
<dd>Either an integer or a character string.<ul>
<li>If an integer (n &gt; 0), the function will first sample <code>n</code> distinct
<code>person_id</code>s from the <code>person</code> table and then subset the input tables to
those subjects.</li>
<li>If a character string, it must be the name of a cohort in the <code>cdm</code>; in
this case, the input tables are subset to subjects (<code>subject_id</code>) belonging
to that cohort.</li>
<li>Use <code>NULL</code> to disable subsetting (default value).</li>
</ul></dd></dl>

Arguments

Helper for consistent documentation — consistent-doc

<dl>

<dt>cdm</dt>
<dd>A <code>cdm_reference</code> object. Use CDMConnector to create a reference
to a database or omock to create a reference to synthetic data.</dd>


<dt>omopTableName</dt>
<dd>A character vector of the names of the tables to
summarise in the cdm object. Run <code>clinicalTables()</code> to check the
available options.</dd>


<dt>ageGroup</dt>
<dd>A list of age groups to stratify the results by. Each element
represents a specific age range. You can give them specific names, e.g.
<code>ageGroup = list(children = c(0, 17), adult = c(18, Inf))</code>.</dd>


<dt>sex</dt>
<dd>Logical; whether to stratify results by sex (<code>TRUE</code>) or not
(<code>FALSE</code>).</dd>


<dt>facet</dt>
<dd>Columns to face by. Formula format can be provided. See possible
columns to face by with: <code>visOmopResults::tidyColumns()</code>.</dd>


<dt>colour</dt>
<dd>Columns to colour by. See possible columns to colour by with:
<code>visOmopResults::tidyColumns()</code>.</dd>


<dt>interval</dt>
<dd>Time interval to stratify by. It can either be "years",
"quarters", "months" or "overall".</dd>


<dt>sample</dt>
<dd>Either an integer or a character string.<ul>
<li>If an integer (n &gt; 0), the function will first sample <code>n</code> distinct
<code>person_id</code>s from the <code>person</code> table and then subset the input tables to
those subjects.</li>
<li>If a character string, it must be the name of a cohort in the <code>cdm</code>; in
this case, the input tables are subset to subjects (<code>subject_id</code>) belonging
to that cohort.</li>
<li>Use <code>NULL</code> to disable subsetting (default value).</li>
</ul></dd>

</dl>

consistent-doc: Helper for consistent documentation

Description

Arguments