sim_inframe

Calculate prob for study sites using table operations

Monitoring reporting rates of subject-level clinical events (e.g.
adverse events, protocol deviations) reported by clinical trial sites is an
important aspect of risk-based quality monitoring strategy. Sites that are
under-reporting or over-reporting events can be detected using bootstrap
simulations during which patients are redistributed between sites. Site-specific
distributions of event reporting rates are generated that are used to assign
probabilities to the observed reporting rates.
(Koneswarakantha 2024 <doi:10.1007/s43441-024-00631-8>).

Bjoern Koneswarakantha

simaerep

Detect Clinical Trial Sites Over- or Under-Reporting Clinical
Events

F. Hoffmann-La Roche Ltd 

sim_inframe function

<dl><dt>df_visit</dt>
<dd>Data frame with columns: study_id, site_number, patnum, visit,
n_ae.</dd>
<dt>r</dt>
<dd>Integer or tbl_object, number of repetitions for bootstrap
simulation. Pass a tbl object referring to a table with one column and as
many rows as desired repetitions. Default: 1000.</dd>
<dt>df_site, </dt>
<dd>dataframe as returned be <code>site_aggr()</code>, Will switch to visit_med75.
Default: NULL</dd>
<dt>event_names</dt>
<dd>vector, contains the event names, default = "event"</dd></dl>

Arguments

Calculate prob for study sites using table operations — sim_inframe

<dl>

<dt>df_visit</dt>
<dd>Data frame with columns: study_id, site_number, patnum, visit,
n_ae.</dd>


<dt>r</dt>
<dd>Integer or tbl_object, number of repetitions for bootstrap
simulation. Pass a tbl object referring to a table with one column and as
many rows as desired repetitions. Default: 1000.</dd>


<dt>df_site, </dt>
<dd>dataframe as returned be <code>site_aggr()</code>, Will switch to visit_med75.
Default: NULL</dd>


<dt>event_names</dt>
<dd>vector, contains the event names, default = "event"</dd>

</dl>

sim_inframe: Calculate prob for study sites using table operations

Description

Usage

Arguments

Examples