Simulate visit level data from a portfolio configuration.
sim_test_data_portfolio(df_config, parallel = FALSE, progress = TRUE)dataframe as returned by get_config
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…¹ max_v…² patnum visit n_ae
#> <chr> <dbl> <chr> <dbl> <dbl> <chr> <int> <int>
#> 1 0001 0.355 0001 4.23 19.9 0001 1 1
#> 2 0001 0.355 0001 4.23 19.9 0001 2 2
#> 3 0001 0.355 0001 4.23 19.9 0001 3 2
#> 4 0001 0.355 0001 4.23 19.9 0001 4 2
#> 5 0001 0.355 0001 4.23 19.9 0001 5 2
#> 6 0001 0.355 0001 4.23 19.9 0001 6 2
#> 7 0001 0.355 0001 4.23 19.9 0001 7 2
#> 8 0001 0.355 0001 4.23 19.9 0001 8 3
#> 9 0001 0.355 0001 4.23 19.9 0001 9 3
#> 10 0001 0.355 0001 4.23 19.9 0001 10 3
#> # … with 3,719 more rows, and abbreviated variable names ¹max_visit_sd,
#> # ²max_visit_mean
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…¹ site_…² n_pat n_pat…³ visit…⁴ mean_…⁵ mean_…⁶ n_pat…⁷ extra…⁸ frac_…⁹
#> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 0001 0001 10 8 21 6.25 7.75 28 0 0
#> 2 0001 0001 10 8 21 3.12 7.75 28 0 0.222
#> 3 0001 0001 10 8 21 0 7.75 28 0 0.222
#> 4 0001 0001 10 8 21 3.12 7.30 28 1 0.333
#> 5 0001 0001 10 8 21 0 6.86 28 1 0.333
#> 6 0001 0001 10 8 21 3.12 6.79 28 2 0.444
#> 7 0001 0001 10 8 21 0 5.82 28 2 0.444
#> 8 0001 0002 10 10 14 3.8 4.74 74 0 0
#> 9 0001 0002 10 10 14 1.9 4.74 74 0 0.119
#> 10 0001 0002 10 10 14 0 4.74 74 0 0.119
#> # … with 130 more rows, 4 more variables: ur_rate <dbl>, prob_low <dbl>,
#> # prob_low_adj <dbl>, prob_low_prob_ur <dbl>, and abbreviated variable names
#> # ¹study_id, ²site_number, ³n_pat_with_med75, ⁴visit_med75,
#> # ⁵mean_ae_site_med75, ⁶mean_ae_study_med75, ⁷n_pat_with_med75_study,
#> # ⁸extra_ur_sites, ⁹frac_pat_with_ur
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
#> # … with 17 more rows
# }