simaerep (version 0.7.0)

sim_test_data_study: simulate study test data

Description

evenly distributes a number of given patients across a number of given sites. Then simulates ae development of each patient reducing the number of reported AEs for patients distributed to AE-under-reporting sites.

Usage

sim_test_data_study(
  n_pat = 1000,
  n_sites = 20,
  frac_site_with_ur = 0,
  ur_rate = 0,
  max_visit_mean = 20,
  max_visit_sd = 4,
  ae_per_visit_mean = c(0.5),
  ae_rates = c(NULL),
  event_names = list("ae")
)

Value

tibble with columns site_number, patnum, is_ur, max_visit_mean, max_visit_sd, ae_per_visit_mean, visit, n_ae

Arguments

n_pat

integer, number of patients, Default: 1000

n_sites

integer, number of sites, Default: 20

frac_site_with_ur

fraction of AE under-reporting sites, Default: 0

ur_rate

AE under-reporting rate, will lower mean ae per visit used to simulate patients at sites flagged as AE-under-reporting. Negative Values will simulate over-reporting., Default: 0

max_visit_mean

mean of the maximum number of visits of each patient, Default: 20

max_visit_sd

standard deviation of maximum number of visits of each patient, Default: 4

ae_per_visit_mean

mean event per visit per patient, Default: 0.5

ae_rates

vector with visit-specific event rates, Default: Null

event_names

vector, contains the event names, default = "ae"

Details

maximum visit number will be sampled from normal distribution with characteristics derived from max_visit_mean and max_visit_sd, while the ae per visit will be sampled from a poisson distribution described by ae_per_visit_mean.

Examples

set.seed(1)
df_visit <- sim_test_data_study(n_pat = 100, n_sites = 5)
df_visit[which(df_visit$patnum == "P000001"),]
df_visit <- sim_test_data_study(n_pat = 100, n_sites = 5,
    frac_site_with_ur = 0.2, ur_rate = 0.5)
df_visit[which(df_visit$patnum == "P000001"),]
ae_rates <- c(0.7, rep(0.5, 8), rep(0.3, 5))
sim_test_data_study(n_pat = 100, n_sites = 5, ae_rates = ae_rates)