simulate AE under-reporting probabilities
simaerep(
df_visit,
param_site_aggr = list(method = "med75_adj", min_pat_pool = 0.2),
param_sim_sites = list(r = 1000, poisson_test = FALSE, prob_lower = TRUE),
param_eval_sites = list(method = "BH"),
progress = TRUE,
check = TRUE,
env = parent.frame(),
under_only = TRUE
)data frame with columns: study_id, site_number, patnum, visit, n_ae
list of parameters passed to site_aggr(), Default: list(method = "med75_adj", min_pat_pool = 0.2)
list of parameters passed to sim_sites(), Default: list(r = 1000, poisson_test = FALSE, prob_lower = TRUE)
list of parameters passed to eval_sites(), Default: list(method = "BH")
logical, display progress bar, Default = TRUE
logical, perform data check and attempt repair with check_df_visit(), computationally expensive on large data sets. Default: TRUE
optional, provide environment of original visit data, Default: parent.frame()
logical compute under-reporting probabilities only, superseeds under_only parameter passed to eval_sites() and sim_sites(), Default: TRUE
simaerep object
executes site_aggr(), sim_sites() and eval_sites() on original visit data and stores all intermediate results. Stores lazy reference to original visit data for facilitated plotting using generic plot(x).
df_visit <- sim_test_data_study(
n_pat = 100,
n_sites = 5,
frac_site_with_ur = 0.4,
ur_rate = 0.6
)
df_visit$study_id <- "A"
aerep <- simaerep(df_visit)
aerep
#> simaerep object:
#> Check aerep$df_eval prob_low_prob_ur column for AE under-reporting probabilites.
#> Plot results using plot() generic.
str(aerep)
#> List of 7
#> $ visit :List of 3
#> ..$ dim : int [1:2] 1969 9
#> ..$ df_summary: tibble [1 × 5] (S3: tbl_df/tbl/data.frame)
#> .. ..$ n_studies : int 1
#> .. ..$ n_sites : int 5
#> .. ..$ n_patients: int 100
#> .. ..$ n_visits : int 1969
#> .. ..$ n_aes : int 703
#> ..$ str_call : chr "df_visit"
#> ..- attr(*, "class")= chr "orivisit"
#> $ df_site : tibble [5 × 6] (S3: tbl_df/tbl/data.frame)
#> ..$ study_id : chr [1:5] "A" "A" "A" "A" ...
#> ..$ site_number : chr [1:5] "S0001" "S0002" "S0003" "S0004" ...
#> ..$ n_pat : int [1:5] 20 20 20 20 20
#> ..$ n_pat_with_med75 : int [1:5] 17 18 17 20 16
#> ..$ visit_med75 : Named num [1:5] 17 15 16 15 16
#> .. ..- attr(*, "names")= chr [1:5] "80%" "80%" "80%" "80%" ...
#> ..$ mean_ae_site_med75: num [1:5] 3.06 2.56 8.53 6.4 8.38
#> $ df_sim_sites : tibble [5 × 9] (S3: tbl_df/tbl/data.frame)
#> ..$ study_id : chr [1:5] "A" "A" "A" "A" ...
#> ..$ site_number : chr [1:5] "S0001" "S0002" "S0003" "S0004" ...
#> ..$ n_pat : int [1:5] 20 20 20 20 20
#> ..$ n_pat_with_med75 : int [1:5] 17 18 17 20 16
#> ..$ visit_med75 : Named num [1:5] 17 15 16 15 16
#> .. ..- attr(*, "names")= chr [1:5] "80%" "80%" "80%" "80%" ...
#> ..$ mean_ae_site_med75 : num [1:5] 3.06 2.56 8.53 6.4 8.38
#> ..$ mean_ae_study_med75 : num [1:5] 7.03 6.18 5.23 5.19 5.31
#> ..$ n_pat_with_med75_study: int [1:5] 59 72 66 70 67
#> ..$ prob_low : num [1:5] 0 0 1 1 1
#> $ df_eval : tibble [5 × 11] (S3: tbl_df/tbl/data.frame)
#> ..$ study_id : chr [1:5] "A" "A" "A" "A" ...
#> ..$ site_number : chr [1:5] "S0001" "S0002" "S0003" "S0004" ...
#> ..$ n_pat : int [1:5] 20 20 20 20 20
#> ..$ n_pat_with_med75 : int [1:5] 17 18 17 20 16
#> ..$ visit_med75 : Named num [1:5] 17 15 16 15 16
#> .. ..- attr(*, "names")= chr [1:5] "80%" "80%" "80%" "80%" ...
#> ..$ mean_ae_site_med75 : num [1:5] 3.06 2.56 8.53 6.4 8.38
#> ..$ mean_ae_study_med75 : num [1:5] 7.03 6.18 5.23 5.19 5.31
#> ..$ n_pat_with_med75_study: int [1:5] 59 72 66 70 67
#> ..$ prob_low : num [1:5] 0 0 1 1 1
#> ..$ prob_low_adj : num [1:5] 0 0 1 1 1
#> ..$ prob_low_prob_ur : num [1:5] 1 1 0 0 0
#> $ param_site_aggr :List of 2
#> ..$ method : chr "med75_adj"
#> ..$ min_pat_pool: num 0.2
#> $ param_sim_sites :List of 4
#> ..$ r : num 1000
#> ..$ poisson_test: logi FALSE
#> ..$ prob_lower : logi TRUE
#> ..$ under_only : logi TRUE
#> $ param_eval_sites:List of 2
#> ..$ method : chr "BH"
#> ..$ under_only: logi TRUE
#> - attr(*, "class")= chr "simaerep"