This function generates a list of tibbles representing the Danish health registers and the data necessary to run the algorithm. The dataset contains individuals who should not be included in the final classified cohort.
Value
A named list of 9 tibble::tibble() objects, each representing a
different health register: bef, lmdb, lpr_adm, lpr_diag,
kontakter, diagnoser, sysi, sssy, and lab_forsker.
Details
The generated data is used in testthat tests to ensure the algorithm
behaves as expected under a wide range of conditions, but it is also intended
to be explored by users to better understand how the algorithm logic works
and to be shown in the documentation.
Examples
non_cases()
#> $bef
#> # A tibble: 7 × 3
#> pnr koen foed_dato
#> <chr> <int> <chr>
#> 1 nc_pcos_1 2 19800101
#> 2 nc_pcos_2 2 19800101
#> 3 nc_pcos_3 2 19800101
#> 4 nc_preg_1 2 19800101
#> 5 nc_preg_2 2 19800101
#> 6 nc_preg_3 2 19800101
#> 7 nc_preg_4 2 19800101
#>
#> $lmdb
#> # A tibble: 7 × 6
#> pnr volume eksd atc apk indo
#> <chr> <dbl> <chr> <chr> <dbl> <chr>
#> 1 nc_pcos_1 10 20210101 A10BA02 5 0000276
#> 2 nc_pcos_2 10 20190101 A10BA02 5 0000276
#> 3 nc_pcos_3 10 20190101 A10BA02 5 0000276
#> 4 nc_preg_1 10 20180101 A10 5 0000000
#> 5 nc_preg_2 10 20180101 A10 5 0000000
#> 6 nc_preg_3 10 20200101 A10 5 0000000
#> 7 nc_preg_4 10 20200101 A10 5 0000000
#>
#> $lpr_adm
#> # A tibble: 7 × 4
#> pnr c_spec recnum d_inddto
#> <chr> <chr> <chr> <chr>
#> 1 nc_pcos_1 08 1 20180101
#> 2 nc_pcos_2 08 1 20170101
#> 3 nc_pcos_3 08 1 20170101
#> 4 nc_preg_1 08 1 20180101
#> 5 nc_preg_2 08 1 20180101
#> 6 nc_preg_1 08 2 20180101
#> 7 nc_preg_2 08 3 20180101
#>
#> $lpr_diag
#> # A tibble: 3 × 3
#> recnum c_diag c_diagtype
#> <chr> <chr> <chr>
#> 1 1 149 A
#> 2 2 DO00 A
#> 3 3 DZ33 A
#>
#> $kontakter
#> # A tibble: 7 × 4
#> cpr dw_ek_kontakt hovedspeciale_ans dato_start
#> <chr> <chr> <chr> <chr>
#> 1 nc_pcos_1 1 medicinsk endokrinologi 20210101
#> 2 nc_pcos_2 1 medicinsk endokrinologi 20190101
#> 3 nc_pcos_3 1 medicinsk endokrinologi 20190101
#> 4 nc_preg_3 1 abc 20200101
#> 5 nc_preg_4 1 abc 20200101
#> 6 nc_preg_3 2 abc 20200101
#> 7 nc_preg_4 3 abc 20200101
#>
#> $diagnoser
#> # A tibble: 3 × 4
#> dw_ek_kontakt diagnosekode diagnosetype senere_afkraeftet
#> <chr> <chr> <chr> <chr>
#> 1 1 DI10 A Nej
#> 2 2 DO00 A Nej
#> 3 3 DZ33 A Nej
#>
#> $sysi
#> # A tibble: 7 × 4
#> pnr barnmak speciale honuge
#> <chr> <int> <chr> <chr>
#> 1 nc_pcos_1 0 53 2101
#> 2 nc_pcos_2 0 53 1901
#> 3 nc_pcos_3 0 53 1901
#> 4 nc_preg_1 0 53 2001
#> 5 nc_preg_2 0 53 2001
#> 6 nc_preg_3 0 53 2001
#> 7 nc_preg_4 0 53 2001
#>
#> $sssy
#> # A tibble: 7 × 4
#> pnr barnmak speciale honuge
#> <chr> <int> <chr> <chr>
#> 1 nc_pcos_1 0 53 2101
#> 2 nc_pcos_2 0 53 1901
#> 3 nc_pcos_3 0 53 1901
#> 4 nc_preg_1 0 53 2001
#> 5 nc_preg_2 0 53 2001
#> 6 nc_preg_3 0 53 2001
#> 7 nc_preg_4 0 53 2001
#>
#> $lab_forsker
#> # A tibble: 7 × 4
#> patient_cpr samplingdate analysiscode value
#> <chr> <chr> <chr> <dbl>
#> 1 nc_pcos_1 20210101 NPU27300 48
#> 2 nc_pcos_2 20190101 NPU03835 6.5
#> 3 nc_pcos_3 20190101 NPU03835 6.5
#> 4 nc_preg_1 20170301 NPU27300 48
#> 5 nc_preg_2 20180301 NPU03835 6.5
#> 6 nc_preg_3 20190301 NPU03835 6.5
#> 7 nc_preg_4 20200301 NPU27300 48
#>