Simulate visit level data from a portfolio configuration.
sim_test_data_portfolio(
df_config,
df_ae_rates = NULL,
parallel = FALSE,
progress = TRUE
)dataframe as returned by get_config
dataframe with ae rates. Default: NULL
logical activate parallel processing, see details, Default: FALSE
logical, Default: TRUE
dataframe with the following columns:
study identification
mean AE per visit per study
site
standard deviation of maximum patient visits per site
mean of maximum patient visits per site
number of patients
visit number
cumulative sum of AEs
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)
sim_test_data_study
get_config
sim_test_data_portfolio
sim_ur_scenarios
get_portf_perf
# \donttest{
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
#> # A tibble: 20 × 6
#> study_id ae_per_visit_mean site_number max_visit_sd max_visit_mean n_pat
#> <chr> <dbl> <chr> <dbl> <dbl> <int>
#> 1 0001 0.355 0001 4.23 19.9 10
#> 2 0001 0.355 0002 4.13 19.8 10
#> 3 0001 0.355 0003 5.03 19.3 10
#> 4 0001 0.355 0004 4.40 20 10
#> 5 0001 0.355 0005 3.77 20.3 10
#> 6 0001 0.355 0006 3.58 17.2 10
#> 7 0001 0.355 0007 5.10 19.6 10
#> 8 0001 0.355 0008 3.74 19.2 10
#> 9 0001 0.355 0009 4.40 19.6 10
#> 10 0001 0.355 0010 4.50 19.4 10
#> 11 0002 0.505 0001 1.51 17.6 10
#> 12 0002 0.505 0002 2.99 20.5 10
#> 13 0002 0.505 0003 3.59 19.7 10
#> 14 0002 0.505 0004 2.70 17.8 10
#> 15 0002 0.505 0005 4.72 21.1 10
#> 16 0002 0.505 0006 5.70 18.9 10
#> 17 0002 0.505 0007 2.68 20.5 10
#> 18 0002 0.505 0008 4.28 19.1 10
#> 19 0002 0.505 0009 3.44 18.5 10
#> 20 0002 0.505 0010 4.86 20.1 10
df_portf <- sim_test_data_portfolio(df_config)
df_portf
#> # A tibble: 3,729 × 8
#> study_id ae_per_visit_mean site_number max_visit_sd max_visit_mean patnum
#> <chr> <dbl> <chr> <dbl> <dbl> <chr>
#> 1 0001 0.355 0001 4.23 19.9 0001
#> 2 0001 0.355 0001 4.23 19.9 0001
#> 3 0001 0.355 0001 4.23 19.9 0001
#> 4 0001 0.355 0001 4.23 19.9 0001
#> 5 0001 0.355 0001 4.23 19.9 0001
#> 6 0001 0.355 0001 4.23 19.9 0001
#> 7 0001 0.355 0001 4.23 19.9 0001
#> 8 0001 0.355 0001 4.23 19.9 0001
#> 9 0001 0.355 0001 4.23 19.9 0001
#> 10 0001 0.355 0001 4.23 19.9 0001
#> # ℹ 3,719 more rows
#> # ℹ 2 more variables: visit <int>, n_ae <int>
df_scen <- sim_ur_scenarios(df_portf,
extra_ur_sites = 2,
ur_rate = c(0.5, 1))
#> aggregating site level
#> prepping for simulation
#> generating scenarios
#> getting under-reporting stats
#> evaluating stats
df_scen
#> # A tibble: 140 × 14
#> study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75
#> <chr> <chr> <int> <int> <dbl> <dbl>
#> 1 0001 0001 10 8 21 6.25
#> 2 0001 0001 10 8 21 3.12
#> 3 0001 0001 10 8 21 0
#> 4 0001 0001 10 8 21 3.12
#> 5 0001 0001 10 8 21 0
#> 6 0001 0001 10 8 21 3.12
#> 7 0001 0001 10 8 21 0
#> 8 0001 0002 10 10 14 3.8
#> 9 0001 0002 10 10 14 1.9
#> 10 0001 0002 10 10 14 0
#> # ℹ 130 more rows
#> # ℹ 8 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>,
#> # extra_ur_sites <dbl>, frac_pat_with_ur <dbl>, ur_rate <dbl>,
#> # prob_low <dbl>, prob_low_adj <dbl>, prob_low_prob_ur <dbl>
df_perf <- get_portf_perf(df_scen)
df_perf
#> # A tibble: 27 × 5
#> fpr thresh extra_ur_sites ur_rate tpr
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.001 0.916 0 0 0.05
#> 2 0.001 0.916 1 0 0.05
#> 3 0.001 0.916 2 0 0.05
#> 4 0.001 0.916 0 0.5 1
#> 5 0.001 0.916 1 0.5 1
#> 6 0.001 0.916 2 0.5 1
#> 7 0.001 0.916 0 1 1
#> 8 0.001 0.916 1 1 1
#> 9 0.001 0.916 2 1 1
#> 10 0.01 0.913 0 0 0.05
#> # ℹ 17 more rows
# }