simaerep (version 1.0.0)

sim_test_data_study: simulate study test data

Description

evenly distributes a number of given patients across a number of given sites. Then simulates event reporting of each patient reducing the number of reported events for patients distributed to event-under-reporting sites.

Usage

sim_test_data_study(
  n_pat = 1000,
  n_sites = 20,
  ratio_out = 0,
  factor_event_rate = 0,
  max_visit_mean = 20,
  max_visit_sd = 4,
  event_rates = dgamma(seq(1, 20, 0.5), shape = 5, rate = 2) * 5 + 0.1,
  event_names = c("event"),
  study_id = "A"
)

Value

tibble with columns site_id, patient_id, is_out, max_visit_mean, max_visit_sd, event_per_visit_mean, visit, n_event

Arguments

n_pat

integer, number of patients, Default: 1000

n_sites

integer, number of sites, Default: 20

ratio_out

ratio of sites with outlier, Default: 0

factor_event_rate

event reporting rate factor for site outlier, will modify mean event per visit rate used for outlier sites. Negative Values will simulate under-reporting, positive values over-reporting, e.g. -0.4 -> 40% under-reporting, +0.4 -> 40% 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

event_rates

list or vector with visit-specific event rates. Use list for multiple event names, Default: dgamma(seq(1, 20, 0.5), shape = 5, rate = 2) * 5 + 0.1

event_names

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

study_id

character, Default: "A"

Details

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

Examples

set.seed(1)
# no outlier
df_visit <- sim_test_data_study(n_pat = 100, n_sites = 5)
df_visit[which(df_visit$patient_id == "P000001"),]

# under-reporting outlier
df_visit <- sim_test_data_study(n_pat = 100, n_sites = 5,
    ratio_out = 0.2, factor_event_rate = -0.5)
df_visit[which(df_visit$patient_id == "P000001"),]

# constant event rates
sim_test_data_study(n_pat = 100, n_sites = 5, event_rates = 0.5)

# non-constant event rates for two event types
event_rates_ae <- c(0.7, rep(0.5, 8), rep(0.3, 5))
event_rates_pd <- c(0.3, rep(0.4, 6), rep(0.1, 5))

sim_test_data_study(
n_pat = 100,
n_sites = 5,
event_names = c("ae", "pd"),
event_rates = list(event_rates_ae, event_rates_pd)
)