Most suitable visual representation of the AE under-reporting statistics.

plot_study(
df_visit,
df_site,
df_eval,
study,
df_al = NULL,
n_sites = 16,
pval = FALSE
)

## Arguments

df_visit dataframe, created by sim_sites() dataframe created by site_aggr() dataframe created by eval_sites() study dataframe containing study_id, site_number, alert_level_site, alert_level_study (optional), Default: NA integer number of most at risk sites, Default: 16 logical show p-value, Default:FALSE

ggplot

## Details

Left panel shows mean AE reporting per site (lightblue and darkblue lines) against mean AE reporting of the entire study (golden line). Single sites are plotted in descending order by AE under-reporting probability on the right panel in which grey lines denote cumulative AE count of single patients. Grey dots in the left panel plot indicate sites that were picked for single plotting. AE under-reporting probability of dark blue lines crossed threshold of 95%. Numbers in the upper left corner indicate the ratio of patients that have been used for the analysis against the total number of patients. Patients that have not been on the study long enough to reach the evaluation point (visit_med75) will be ignored.

## Examples

df_visit <- sim_test_data_study(n_pat = 1000, n_sites = 10,
frac_site_with_ur = 0.2, ur_rate = 0.15, max_visit_sd = 8)

df_visit\$study_id <- "A"
df_site <- site_aggr(df_visit)

df_sim_sites <- sim_sites(df_site, df_visit, r = 100)

df_eval <- eval_sites(df_sim_sites)

plot_study(df_visit, df_site, df_eval, study = "A")