sim_sites {simaerep}

Calculate prob_lower and poisson.test pvalue for study sites.

Description

Collects the number of AEs of all eligible patients that meet visit_med75 criteria of site. Then calculates poisson.test pvalue and bootstrapped probability of having a lower mean value.

Usage

sim_sites(
  df_site,
  df_visit,
  r = 1000,
  poisson_test = TRUE,
  prob_lower = TRUE,
  progress = TRUE,
  check = TRUE,
  under_only = TRUE
)

Arguments

df_site

dataframe created by site_aggr

df_visit

dataframe, created by sim_sites

r

integer, denotes number of simulations, default = 1000

poisson_test

logical, calculates poisson.test pvalue

prob_lower

logical, calculates probability for getting a lower value

progress

logical, display progress bar, Default = TRUE

check

logical, perform data check and attempt repair with

under_only

compute under-reporting probabilities only, default = TRUE check_df_visit(), computationally expensive on large data sets. Default: TRUE

Value

dataframe with the following columns:

study_id

study identification

site_number

site identification

n_pat

number of patients at site

visit_med75

median(max(visit)) * 0.75

n_pat_with_med75

number of patients at site with med75

mean_ae_site_med75

mean AE at visit_med75 site level

mean_ae_study_med75

mean AE at visit_med75 study level

n_pat_with_med75_study

number of patients at study with med75 excl. site

pval

p-value as returned by poisson.test

prob_low

bootstrapped probability for having mean_ae_site_med75 or lower

See Also

sim_sites, site_aggr, pat_pool, prob_lower_site_ae_vs_study_ae, poiss_test_site_ae_vs_study_ae, sim_sites, prep_for_sim

Examples

df_visit <- sim_test_data_study(
   n_pat = 100,
   n_sites = 5,
   frac_site_with_ur = 0.4,
   ur_rate = 0.2
)

df_visit$study_id <- "A"

df_site <- site_aggr(df_visit)

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

df_sim_sites %>%
 knitr::kable(digits = 2)