Correct under-reporting probabilities using p.adjust.
Usage
eval_sites(
df_sim_sites,
method = "BH",
under_only = TRUE,
event_names = c("ae"),
...
)Arguments
- df_sim_sites
dataframe generated by
sim_sites- method
character, passed to stats::p.adjust(), if NULL
- under_only
compute under-reporting probabilities only, default = TRUE check_df_visit(), computationally expensive on large data sets. Default: TRUE
- event_names
vector, contains the event names, default = "ae" eval_sites_deprecated() is used instead, Default = "BH"
- ...
use to pass r_sim_sites parameter to eval_sites_deprecated()
Value
dataframe with the following columns:
- study_id
study identification
- site_number
site identification
- visit_med75
median(max(visit)) * 0.75
- mean_ae_site_med75
mean AE at visit_med75 site level
- mean_ae_study_med75
mean AE at visit_med75 study level
- pval
p-value as returned by
poisson.test- prob_low
bootstrapped probability for having mean_ae_site_med75 or lower
- pval_adj
adjusted p-values
- prob_low_adj
adjusted bootstrapped probability for having mean_ae_site_med75 or lower
- pval_prob_ur
probability under-reporting as 1 - pval_adj, poisson.test (use as benchmark)
- prob_low_prob_ur
probability under-reporting as 1 - prob_low_adj, bootstrapped (use)
Examples
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"
df_site <- site_aggr(df_visit)
df_sim_sites <- sim_sites(df_site, df_visit, r = 100)
df_eval <- eval_sites(df_sim_sites)
df_eval
#> # A tibble: 5 × 14
#> study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75
#> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 A S0001 20 16 15 2.62
#> 2 A S0002 20 16 16 2.75
#> 3 A S0003 20 19 15 6.37
#> 4 A S0004 20 16 16 7.81
#> 5 A S0005 20 17 16 8.59
#> # ℹ 8 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>,
#> # pval <dbl>, prob_low <dbl>, pval_adj <dbl>, pval_prob_ur <dbl>,
#> # prob_low_adj <dbl>, prob_low_prob_ur <dbl>