Get Portfolio configuration from a df_visit input dataframe. Will
. filter studies with only a few sites and patients and will anonymize IDs.
. Portfolio configuration can be
used by sim_test_data_portfolio to generate data for an
artificial portfolio.
Usage
get_portf_config(
df_visit,
check = TRUE,
min_pat_per_study = 100,
min_sites_per_study = 10,
anonymize = TRUE,
pad_width = 4
)Arguments
- df_visit
input dataframe with columns study_id, site_id, patient_id, visit, n_events. Can also be a lazy database table.
- check
logical, perform standard checks on df_visit, Default: TRUE
- min_pat_per_study
minimum number of patients per study, Default: 100
- min_sites_per_study
minimum number of sites per study, Default: 10
- anonymize
logical, Default: TRUE
- pad_width
padding width for newly created IDs, Default: 4
Value
dataframe with the following columns:
- study_id
study identification
- event_per_visit_mean
mean event per visit per study
- site_id
site
- max_visit_sd
standard deviation of maximum patient visits per site
- max_visit_mean
mean of maximum patient visits per site
- n_pat
number of patients
See also
sim_test_data_study
get_portf_config
sim_test_data_portfolio
Examples
df_visit1 <- sim_test_data_study(n_pat = 100, n_sites = 10,
ratio_out = 0.4, factor_event_rate = - 0.6,
study_id = "A")
df_visit2 <- sim_test_data_study(n_pat = 100, n_sites = 10,
ratio_out = 0.2, factor_event_rate = - 0.1,
study_id = "B")
df_visit <- dplyr::bind_rows(df_visit1, df_visit2)
get_portf_config(df_visit)
#> # A tibble: 20 × 6
#> study_id event_per_visit_mean site_id max_visit_sd max_visit_mean n_pat
#> <chr> <dbl> <chr> <dbl> <dbl> <int>
#> 1 0001 0.455 0001 2.86 20.2 10
#> 2 0001 0.455 0002 3.98 20.1 10
#> 3 0001 0.455 0003 5.87 19.6 10
#> 4 0001 0.455 0004 4.38 18.1 10
#> 5 0001 0.455 0005 5.43 18.9 10
#> 6 0001 0.455 0006 3.74 19.7 10
#> 7 0001 0.455 0007 3.08 18.8 10
#> 8 0001 0.455 0008 4.12 19.4 10
#> 9 0001 0.455 0009 3.49 22.2 10
#> 10 0001 0.455 0010 2.23 20.9 10
#> 11 0002 0.576 0001 3.98 18.5 10
#> 12 0002 0.576 0002 3.03 19.6 10
#> 13 0002 0.576 0003 2.86 20.8 10
#> 14 0002 0.576 0004 4.88 20.5 10
#> 15 0002 0.576 0005 4.90 21.3 10
#> 16 0002 0.576 0006 3.74 19.8 10
#> 17 0002 0.576 0007 6.07 20 10
#> 18 0002 0.576 0008 4.38 18.6 10
#> 19 0002 0.576 0009 4.75 17.9 10
#> 20 0002 0.576 0010 3.14 19.1 10
# \donttest{
# Database example
con <- DBI::dbConnect(duckdb::duckdb(), dbdir = ":memory:")
dplyr::copy_to(con, df_visit, "visit")
tbl_visit <- dplyr::tbl(con, "visit")
get_portf_config(tbl_visit)
#> # A tibble: 20 × 6
#> study_id event_per_visit_mean site_id max_visit_sd max_visit_mean n_pat
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 0001 0.455 0001 2.86 20.2 10
#> 2 0001 0.455 0010 2.23 20.9 10
#> 3 0001 0.455 0009 3.49 22.2 10
#> 4 0001 0.455 0004 4.38 18.1 10
#> 5 0002 0.576 0001 3.98 18.5 10
#> 6 0002 0.576 0010 3.14 19.1 10
#> 7 0002 0.576 0009 4.75 17.9 10
#> 8 0002 0.576 0004 4.88 20.5 10
#> 9 0001 0.455 0007 3.08 18.8 10
#> 10 0001 0.455 0008 4.12 19.4 10
#> 11 0001 0.455 0005 5.43 18.9 10
#> 12 0002 0.576 0007 6.07 20 10
#> 13 0002 0.576 0008 4.38 18.6 10
#> 14 0002 0.576 0005 4.90 21.3 10
#> 15 0001 0.455 0002 3.98 20.1 10
#> 16 0001 0.455 0006 3.74 19.7 10
#> 17 0001 0.455 0003 5.87 19.6 10
#> 18 0002 0.576 0002 3.03 19.6 10
#> 19 0002 0.576 0006 3.74 19.8 10
#> 20 0002 0.576 0003 2.86 20.8 10
DBI::dbDisconnect(con)
# }