Plot ae development of study and sites highlighting at risk sites.

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

Usage

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

Arguments

df_visit

dataframe, created by sim_sites()

df_site

dataframe created by site_aggr()

df_eval

dataframe created by eval_sites()

study

study

df_al

dataframe containing study_id, site_number, alert_level_site, alert_level_study (optional), Default: NA

n_sites

integer number of most at risk sites, Default: 16

pval

logical show p-value, Default:FALSE

prob_col

character, denotes probability column, Default: "prob_low_prob_ur"

Value

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

# \donttest{
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")

# }