Detect Clinical Trial Sites Over- or Under-Reporting Clinical Events
Aggregate duplicated visits.
Integrity check for df_visit.
Evaluate sites.
Expose implicitly missing visits.
Get cumulative mean event development
Get df_visit_test mapped
Get df_visit_test
replace cowplot::get_legend, to silence warning Multiple components fo...
Get Portfolio Configuration
Get Portfolio Event Rates Calculates mean event rates per study and vi...
Get site mean ae development.
Get visit_med75.
is orivisit class
is simaerep class
Calculate Max Rank
create orivisit object
benjamini hochberg p value correction using table operations
Aggregate visit to patient level.
Create a study specific patient pool for sampling
Pipe operator
Plots AE per site as dots.
Plot simulation example.
Plot multiple simulation examples.
Plot ae development of study and sites highlighting at risk sites.
Plot patient visits against visit_med75.
plot AE under-reporting simulation results
Poisson test for vector with site AEs vs vector with study AEs.
Prepare data for simulation.
Print method for orivisit objects
Print method for simaerep objects
Calculate bootstrapped probability for obtaining a lower site mean AE ...
prune visits to visit_med75 using table operations
Execute a purrr or furrr function with a progress bar.
renames internal simaerep col_names to externally applied colnames
Start simulation after preparation.
Calculate prob for study sites using table operations
simulate under-reporting
simulate patients and events for sites supports constant and non-const...
Calculate prob_lower and poisson.test pvalue for study sites.
simulate test data events
simulate patient event reporting test data
Simulate Portfolio Test Data
simulate study test data
Create simaerep object
Aggregate from visit to site level.
Conditional with_progress.
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>).