Simulate visit level data from a portfolio configuration.
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
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
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)
See also
sim_test_data_study
get_config
sim_test_data_portfolio
sim_ur_scenarios
get_portf_perf
Examples
# \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 extra_ur_sites frac_pat_with_ur ur_rate n_pat
#> <chr> <chr> <dbl> <dbl> <dbl> <int>
#> 1 0001 0001 0 0 0 10
#> 2 0001 0001 0 0.222 0.5 10
#> 3 0001 0001 0 0.222 1 10
#> 4 0001 0001 1 0.333 0.5 10
#> 5 0001 0001 1 0.333 1 10
#> 6 0001 0001 2 0.444 0.5 10
#> 7 0001 0001 2 0.444 1 10
#> 8 0001 0002 0 0 0 10
#> 9 0001 0002 0 0.119 0.5 10
#> 10 0001 0002 0 0.119 1 10
#> # ℹ 130 more rows
#> # ℹ 8 more variables: n_pat_with_med75 <dbl>, visit_med75 <dbl>,
#> # mean_ae_site_med75 <dbl>, mean_ae_study_med75 <dbl>,
#> # n_pat_with_med75_study <int>, 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 0.555 0 0 0.1
#> 2 0.001 0.555 1 0 0.1
#> 3 0.001 0.555 2 0 0.1
#> 4 0.001 0.555 0 0.5 1
#> 5 0.001 0.555 1 0.5 1
#> 6 0.001 0.555 2 0.5 0.9
#> 7 0.001 0.555 0 1 1
#> 8 0.001 0.555 1 1 1
#> 9 0.001 0.555 2 1 1
#> 10 0.01 0.555 0 0 0.1
#> # ℹ 17 more rows
# }