Test function, test applicability of poisson test, by
calculating a the bootstrapped probability of obtaining a specific p-value
or lower, use in combination with get_ecd_values().
sim_studies(
df_visit,
df_site,
r = 100,
poisson_test = TRUE,
prob_lower = TRUE,
r_prob_lower = 1000,
under_only = TRUE,
parallel = FALSE,
keep_ae = FALSE,
min_n_pat_with_med75 = 1,
studies = NULL,
.progress = TRUE
)dataframe
dataframe
integer, denotes number of simulations, Default: 1000
logical, calculates poisson.test pvalue, Default: TRUE
logical, calculates probability for getting a lower value, Default: FALSE
integer, denotes number of simulations for prob_lower value calculation,, Default: 1000
compute under-reporting probabilities only, default = TRUE
logical, see examples for registering parallel processing framework , Default: FALSE
logical, keep ae numbers in output dataframe memory increase roughly 30 percent, Default: F
integer, min number of patients with med75 at site to simulate, Default: 1
vector with study names, Default: NULL
logical, show progress bar
dataframe
Here we simulate study replicates maintaining the same number of sites, patients and visit_med75 by bootstrap resampling, then probabilities for obtaining lower or same mean_ae count and p-values using poisson.test are calculated.
adds column with simulated probabilities for equal or lower mean_ae at visit_med75
# \donttest{
df_visit1 <- sim_test_data_study(n_pat = 100, n_sites = 5,
frac_site_with_ur = 0.4, ur_rate = 0.6)
df_visit1$study_id <- "A"
df_visit2 <- sim_test_data_study(n_pat = 1000, n_sites = 3,
frac_site_with_ur = 0.2, ur_rate = 0.1)
df_visit2$study_id <- "B"
df_visit <- dplyr::bind_rows(df_visit1, df_visit2)
df_site <- site_aggr(df_visit)
sim_studies(df_visit, df_site, r = 3, keep_ae = TRUE)
#> # A tibble: 24 × 10
#> r study_id site_number visit_med75 n_pat_with_med75 n_pat_study n_ae_site
#> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
#> 1 1 A S0001 16 20 68 7,13,7,4…
#> 2 1 A S0002 18 17 57 1,10,4,4…
#> 3 1 A S0003 17 18 63 13,11,13…
#> 4 1 A S0004 14 18 76 3,9,9,9,…
#> 5 1 A S0005 15 18 72 9,4,7,6,…
#> 6 1 B S0001 15 297 597 9,8,5,7,…
#> 7 1 B S0002 15 296 598 14,8,7,1…
#> 8 1 B S0003 15 301 593 9,5,8,6,…
#> 9 2 A S0001 16 20 68 10,7,5,3…
#> 10 2 A S0002 18 17 57 12,12,6,…
#> # ℹ 14 more rows
#> # ℹ 3 more variables: n_ae_study <chr>, pval <dbl>, prob_low <dbl>
# }
if (FALSE) {
# parallel processing -------------------------
library(future)
future::plan(multiprocess)
sim_studies(df_visit, df_site, r = 3, keep_ae = TRUE, parallel = TRUE)
future::plan(sequential)
}