The `adam_db` argument
adam_db_argument.RmdIntroduction
We have decided to use the dm package in
chevron to model the data relations of the
CDISC ADaM datasets. In this vignette we
introduce how we intend to use ADaM data in a relational
model, in R with dm. Note that dm can be
connected to a database.
Sample Data
We remove the ADSL variables in the database of the
non-ADSL datasets and merge the ADSL variables
back as needed.
library(chevron)
#> Registered S3 method overwritten by 'tern':
#> method from
#> tidy.glm broom
library(dm)
#>
#> Attaching package: 'dm'
#> The following object is masked from 'package:stats':
#>
#> filter
drop_adsl_vars <- function(df, adsl) {
df[c("USUBJID", "STUDYID", setdiff(names(df), names(adsl)))]
}
data(syn_data, package = "chevron")
dbl <- c(list(adsl = syn_data$adsl), lapply(syn_data[-1], drop_adsl_vars, adsl = syn_data$adsl))
### Define primary and foreign keys
adam_study_data <- new_dm(dbl) %>%
dm_add_pk(adsl, c("USUBJID", "STUDYID")) %>%
dm_add_fk(adae, c("USUBJID", "STUDYID"), ref_table = "adsl") %>%
dm_add_pk(adae, c("USUBJID", "STUDYID", "ASTDTM", "AETERM", "AESEQ")) %>%
dm_add_fk(adlb, c("USUBJID", "STUDYID"), ref_table = "adsl") %>%
dm_add_pk(adlb, c(
"STUDYID", "USUBJID", "PARAMCD", "BASETYPE", "AVISITN",
"ATPTN", "DTYPE", "ADTM", "LBSEQ", "ASPID"
)) %>%
dm_add_fk(adrs, c("USUBJID", "STUDYID"), ref_table = "adsl") %>%
dm_add_pk(adrs, c("STUDYID", "USUBJID", "PARAMCD", "AVISITN", "ADTM", "RSSEQ")) %>%
dm_add_fk(adsub, c("USUBJID", "STUDYID"), ref_table = "adsl") %>%
dm_add_pk(adsub, c("USUBJID", "STUDYID", "PARAMCD"))
dm_validate(adam_study_data)Note that the keys are defined in the data analysis plan at Roche.
Work With dm Object
List the keys
dm::dm_get_all_fks(adam_study_data)
#> # A tibble: 4 × 5
#> child_table child_fk_cols parent_table parent_key_cols on_delete
#> <chr> <keys> <chr> <keys> <chr>
#> 1 adae USUBJID, STUDYID adsl USUBJID, STUDYID no_action
#> 2 adlb USUBJID, STUDYID adsl USUBJID, STUDYID no_action
#> 3 adrs USUBJID, STUDYID adsl USUBJID, STUDYID no_action
#> 4 adsub USUBJID, STUDYID adsl USUBJID, STUDYID no_action
dm::dm_get_all_pks(adam_study_data)
#> # A tibble: 5 × 3
#> table pk_col autoincrement
#> <chr> <keys> <lgl>
#> 1 adsl USUBJID, STUDYID FALSE
#> 2 adae USUBJID, STUDYID, … (5 total) FALSE
#> 3 adlb STUDYID, USUBJID, … (10 total) FALSE
#> 4 adrs STUDYID, USUBJID, … (6 total) FALSE
#> 5 adsub USUBJID, STUDYID, PARAMCD FALSESubset the data
Further we can subset the data:
The dataset adsl is filtered
db %>%
pull_tbl(adsl) %>%
select(USUBJID, STUDYID, SEX) %>%
head()
#> # A tibble: 6 × 3
#> USUBJID STUDYID SEX
#> <chr> <chr> <fct>
#> 1 AB12345-RUS-3-id-378 AB12345 F
#> 2 AB12345-CHN-11-id-220 AB12345 F
#> 3 AB12345-USA-1-id-45 AB12345 F
#> 4 AB12345-USA-1-id-261 AB12345 F
#> 5 AB12345-NGA-11-id-173 AB12345 F
#> 6 AB12345-CHN-4-id-73 AB12345 FIf one does not need the relational filtering feature then one can
also zoom to the desired table and use filter
adam_study_data %>%
dm_zoom_to(adsl) %>%
filter(SEX == "F") %>%
dm_update_zoomed() %>%
pull_tbl(adsl) %>%
select(USUBJID, STUDYID, SEX) %>%
head()
#> # A tibble: 6 × 3
#> USUBJID STUDYID SEX
#> <chr> <chr> <fct>
#> 1 AB12345-RUS-3-id-378 AB12345 F
#> 2 AB12345-CHN-11-id-220 AB12345 F
#> 3 AB12345-USA-1-id-45 AB12345 F
#> 4 AB12345-USA-1-id-261 AB12345 F
#> 5 AB12345-NGA-11-id-173 AB12345 F
#> 6 AB12345-CHN-4-id-73 AB12345 FMutate Data
dm supports most of the dplyr verbs, for
example mutate
db <- adam_study_data %>%
dm_zoom_to(adsl) %>%
mutate(TEST = paste(SEX, "-", BWGHTSI)) %>%
dm_update_zoomed()
db$adsl$TEST[1:5]
#> [1] "M - 109.5" "M - 111.1" "F - 108.3" "F - 131.1" "M - 83.1"
dm Objects in Chevron
In chevron we use dm objects to combine all
data into one object. The TLG-functions and the functions
returned std_filter and std_mutate require a
dm object to the adam_db argument.
dm objects also are useful to filter the data. For
example filtering for all females also subsets the other datasets as per
relation specification:
adam_study_data %>%
dm_filter(adsl, SEX == "F") %>%
dm_apply_filters() %>%
dm_flatten_to_tbl(adae, adsl) %>%
select(USUBJID, SEX, AESEQ, AETERM, AESEQ) %>%
head()
#> Warning: The `table` argument of `dm_filter()` is deprecated as of dm 1.0.0.
#> ℹ `dm_filter()` now takes named filter expressions, the names correspond to the
#> tables to be filtered. You no longer need to call `dm_apply_filters()` to
#> materialize the filters.
#> # A tibble: 6 × 4
#> USUBJID SEX AESEQ AETERM
#> <chr> <fct> <int> <fct>
#> 1 AB12345-BRA-1-id-141 F 1 trm B.2.1.2.1
#> 2 AB12345-BRA-1-id-141 F 2 trm D.2.1.5.3
#> 3 AB12345-BRA-1-id-141 F 3 trm A.1.1.1.1
#> 4 AB12345-BRA-1-id-141 F 4 trm A.1.1.1.2
#> 5 AB12345-BRA-1-id-141 F 5 trm A.1.1.1.1
#> 6 AB12345-BRA-1-id-141 F 6 trm D.1.1.1.1Get Analysis Data
Vertical Analysis Data
For standard outputs we do know which data and variables we need to create a TLG.
adam_study_data %>%
dm_join_to_tbl(adae, adsl) %>%
select(AESEQ, ARM, SEX)
#> Warning: `dm_join_to_tbl()` was deprecated in dm 0.3.0.
#> ℹ Please use `dm_flatten_to_tbl()` instead.
#> # A tibble: 1,934 × 3
#> AESEQ ARM SEX
#> <int> <fct> <fct>
#> 1 1 A: Drug X M
#> 2 2 A: Drug X M
#> 3 3 A: Drug X M
#> 4 4 A: Drug X M
#> 5 1 C: Combination F
#> 6 2 C: Combination F
#> 7 3 C: Combination F
#> 8 4 C: Combination F
#> 9 5 C: Combination F
#> 10 6 C: Combination F
#> # … with 1,924 more rowsHorizontal
Add variables from adsub to adsl:
library(tidyr)
adsub_wide <- adam_study_data$adsub %>%
filter(PARAMCD %in% c("BWGHTSI", "BECOG")) %>%
select(USUBJID, STUDYID, PARAMCD, AVAL, AVALU) %>%
pivot_wider(names_from = PARAMCD, values_from = c(AVAL, AVALU))
dm(adam_study_data, adsub_wide) %>%
dm_add_fk(adsub_wide, c("USUBJID", "STUDYID"), ref_table = "adsl") %>%
dm_add_pk(adsub_wide, c("USUBJID", "STUDYID")) %>%
dm_join_to_tbl(adsl, adsub_wide)
#> # A tibble: 400 × 68
#> USUBJID STUDYID AVAL_…¹ AVAL_…² AVALU…³ AVALU…⁴ SUBJID SITEID AGE AGEU
#> <chr> <chr> <dbl> <dbl> <fct> <fct> <chr> <chr> <int> <fct>
#> 1 AB12345-BR… AB12345 62.5 3 kg "" id-105 BRA-1 38 YEARS
#> 2 AB12345-BR… AB12345 98.8 3 kg "" id-134 BRA-1 47 YEARS
#> 3 AB12345-BR… AB12345 49.6 1 kg "" id-141 BRA-1 35 YEARS
#> 4 AB12345-BR… AB12345 162. 3 kg "" id-236 BRA-1 32 YEARS
#> 5 AB12345-BR… AB12345 105. 2 kg "" id-265 BRA-1 25 YEARS
#> 6 AB12345-BR… AB12345 53.8 4 kg "" id-42 BRA-1 36 YEARS
#> 7 AB12345-BR… AB12345 60.6 3 kg "" id-65 BRA-1 25 YEARS
#> 8 AB12345-BR… AB12345 63.8 4 kg "" id-93 BRA-1 34 YEARS
#> 9 AB12345-BR… AB12345 144. 3 kg "" id-171 BRA-11 40 YEARS
#> 10 AB12345-BR… AB12345 76.9 3 kg "" id-217 BRA-11 43 YEARS
#> # … with 390 more rows, 58 more variables: SEX <fct>, RACE <fct>, ETHNIC <fct>,
#> # COUNTRY <fct>, DTHFL <fct>, INVID <chr>, INVNAM <chr>, ARM <fct>,
#> # ARMCD <fct>, ACTARM <fct>, ACTARMCD <fct>, TRT01P <fct>, TRT01A <fct>,
#> # TRT02P <fct>, TRT02A <fct>, REGION1 <fct>, STRATA1 <fct>, STRATA2 <fct>,
#> # BMRKR1 <dbl>, BMRKR2 <fct>, ITTFL <fct>, SAFFL <fct>, BMEASIFL <fct>,
#> # BEP01FL <fct>, AEWITHFL <fct>, RANDDT <date>, TRTSDTM <dttm>,
#> # TRTEDTM <dttm>, TRT01SDTM <dttm>, TRT01EDTM <dttm>, TRT02SDTM <dttm>, …