sim_test_data_portfolio {simaerep}

Simulate Portfolio Test Data

Description

Simulate visit level data from a portfolio configuration.

Usage

sim_test_data_portfolio(
  df_config,
  df_ae_rates = NULL,
  parallel = FALSE,
  progress = TRUE
)

Arguments

df_config

dataframe as returned by get_config

df_ae_rates

dataframe with ae rates. Default: NULL

parallel

logical activate parallel processing, see details, Default: FALSE

progress

logical, Default: TRUE

Details

uses sim_test_data_study. We use the furrr package to implement parallel processing as these simulations can take a long time to run. For this to work we need to specify the plan for how the code should run, e.g. 'plan(multisession, workers = 3)

Value

dataframe with the following columns:

study_id

study identification

ae_per_visit_mean

mean AE per visit per study

site_number

site

max_visit_sd

standard deviation of maximum patient visits per site

max_visit_mean

mean of maximum patient visits per site

patnum

number of patients

visit

visit number

n_ae

cumulative sum of AEs

See Also

sim_test_data_study get_config sim_test_data_portfolio sim_ur_scenarios get_portf_perf

Examples

df_visit1 <- sim_test_data_study(n_pat = 100, n_sites = 10,
                                 frac_site_with_ur = 0.4, ur_rate = 0.6)

df_visit1$study_id <- "A"

df_visit2 <- sim_test_data_study(n_pat = 100, n_sites = 10,
                                 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_max <- df_visit %>%
  dplyr::group_by(study_id, site_number, patnum) %>%
  dplyr::summarise(max_visit = max(visit),
            max_ae = max(n_ae),
            .groups = "drop")

df_config <- get_config(df_site_max)

df_config

df_portf <- sim_test_data_portfolio(df_config)

df_portf

df_scen <- sim_ur_scenarios(df_portf,
                            extra_ur_sites = 2,
                            ur_rate = c(0.5, 1))


df_scen

df_perf <- get_portf_perf(df_scen)

df_perf