Validate SDTM Domains • sdtmval

{sdtmval} provides a set of tools to assist statistical programmers in validating Study Data Tabulation Model (SDTM) domain data sets.

Many data cleaning steps and SDTM processes are used repeatedly in different SDTM domain validation scripts. Functionalizing these repetitive tasks allows statistical programmers to focus on coding the unique aspects of a SDTM domain while standardize their code base across studies and domains. This should lead to fewer bugs and improved code readability too. {sdtmval} features include:

Automating the BLFL, DY, EPOCH, SEQ, and STAT methods to create new variables
Imputing and formatting full and partial dates: see vignette("Dates")
Applying specification data such as variable labels, lengths, code mapping, and sorting
Importing EDC and SDTM data from .csv and .sas7bdat files
Writing .xpt files (convenience wrapper for haven::write_xpt())
Converting .Rmd files to .R scripts (convenience wrapper for knitr::purl())
Logging R session information for reproducibility
Data formatting

Installation

You can install the release version of {sdtmval} from CRAN with:

install.packages("sdtmval")

You can install the development version of {sdtmval} from GitHub with:

# install.packages("devtools")
devtools::install_github("skgithub14/sdtmval")

A typical work flow example

In this example work flow, we will import a raw EDC table and transform it into a SDTM domain table. We will use the made-up domain ‘XX’ along with some example data included in {sdtmval}.

# set-up
library(sdtmval)
library(dplyr)

domain <- "XX"

# set working directory to location of sdtmval package example data
work_dir <- system.file("extdata", package = "sdtmval")

The majority of the data needed is in the EDC form/table xx.csv. There are also visit dates in the EDC table vd.csv and study start/end dates in the SDTM table dm.sas7dbat. These can be imported using read_edc_tbls() and read_sdtm_tbls().

# read in EDC tables from the forms XX and VD
edc_tbls <- c("xx", "vd")
edc_dat <- read_edc_tbls(edc_tbls, dir = work_dir)

# read in SDTM domain DM
sdtm_tbls <- c("dm")
sdtm_dat <- read_sdtm_tbls(sdtm_tbls, dir = work_dir)

The raw data looks like this:

STUDYID	USUBJID	VISIT	XXTESTCD	XXORRES
Study 1	Subject 1	Visit 1	T1	1
Study 1	Subject 1	Visit 2	T1	0
Study 1	Subject 1	Visit 3	T1	2
Study 1	Subject 1	Visit 3	T2	100
Study 1	Subject 1	Visit 4	T3	PASS
Study 1	Subject 2	Visit 1	T1	1
Study 1	Subject 2	Visit 2	T1
Study 1	Subject 2	Visit 3	T1	2
Study 1	Subject 2	Visit 3	T2	200
Study 1	Subject 2	Visit 4	T3	FAIL

The next thing we will do is get the relevant information from the SDTM specification for the study. The next set of functions assumes there is a .xlsx file which contains the sheets: ‘Datasets’, ‘XX’, and ‘Codelists’:

‘XX’ gives the variable information for the made-up XX domain. get_data_spec() retrieves this entire tab.
‘Datasets’ contains the key variables by domain. get_key_vars() retrieves the these for desired domain.
‘Codelists’ provides a table of coded/decoded values by variable for all domains. get_codelist() extracts a data frame of coded/decoded values from this sheet just for the variables in desired domain.

spec_fname <- "spec.xlsx"
spec <- get_data_spec(domain = domain, dir = work_dir, filename = spec_fname)
key_vars <- get_key_vars(domain = domain, dir = work_dir, filename = spec_fname)
codelists <- get_codelist(domain = domain, dir = work_dir, filename = spec_fname)

knitr::kable(spec)

Order	Dataset	Variable	Label	Data Type	Length
1	XX	STUDYID	Study Identifier	text	200
2	XX	DOMAIN	Domain Abbreviation	text	200
3	XX	USUBJID	Unique Subject Identifier	text	200
4	XX	XXSEQ	Sequence Number	integer	8
5	XX	XXTESTCD	XX Test Short Name	text	8
6	XX	XXTEST	XX Test Name	text	40
7	XX	XXORRES	Result or Finding in Original Units	text	200
8	XX	XXBLFL	Baseline Flag	text	1
9	XX	VISIT	Visit Name	text	200
10	XX	EPOCH	Epoch	text	200
11	XX	XXDTC	Date/Time of Measurements	datetime	19
12	XX	XXDY	Study Day of XX	integer	8

knitr::kable(codelists)

ID	Term	Decoded Value
XXTESTCD	T1	Test 1
XXTESTCD	T2	Test 2
XXTESTCD	T3	Test 3

key_vars
#> [1] "STUDYID"  "USUBJID"  "XXTESTCD" "VISIT"

Now we will begin creating the SDTM XX domain using the EDC XX form as the basis.

First, it needs some pre-processing because there is extra white space in some of the variables. We also want to turn all NA equivalent values like "" and " " to NA for the entire data set so we have consistent handling of missing values during data processing. The function trim_and_make_blanks_NA() does both of these tasks.

sdtm_xx1 <- trim_and_make_blanks_NA(edc_dat$xx)

Next, using the codelist we retrieved earlier, we can create the XXTEST variable.

# prepare the code list so it can be used by dplyr::recode() 
xxtestcd_codelist <- codelists %>%
  filter(ID == "XXTESTCD") %>%
  select(Term, `Decoded Value`) %>%
  tibble::deframe()

# create XXTEST variable
sdtm_xx2 <- mutate(sdtm_xx1, XXTEST = recode(XXTESTCD, !!!xxtestcd_codelist))

knitr::kable(sdtm_xx2)

STUDYID	USUBJID	VISIT	XXTESTCD	XXORRES	XXTEST
Study 1	Subject 1	Visit 1	T1	1	Test 1
Study 1	Subject 1	Visit 2	T1	0	Test 1
Study 1	Subject 1	Visit 3	T1	2	Test 1
Study 1	Subject 1	Visit 3	T2	100	Test 2
Study 1	Subject 1	Visit 4	T3	PASS	Test 3
Study 1	Subject 2	Visit 1	T1	1	Test 1
Study 1	Subject 2	Visit 2	T1	NA	Test 1
Study 1	Subject 2	Visit 3	T1	2	Test 1
Study 1	Subject 2	Visit 3	T2	200	Test 2
Study 1	Subject 2	Visit 4	T3	FAIL	Test 3

In order to calculate the variables XXBLFL, EPOCH, and XXDY, we need the visit dates from the EDC VD table and the study start/end dates by subject from the SDTM DM table.

sdtm_xx3 <- sdtm_xx2 %>%
  
  # get the VISITDTC column from the EDC VD form
  left_join(edc_dat$vd, by = c("USUBJID", "VISIT")) %>%
  
  # create the XXDTC variable
  rename(XXDTC = VISITDTC) %>%
  
  # get the study start/end dates by subject (RFSTDTC, RFXSTDTC, RFXENDTC)
  left_join(sdtm_dat$dm, by = "USUBJID")

Now, we can proceed with calculating those timing variables using the create_BLFL(), create_EPOCH(), and calc_DY() functions.

sdtm_xx4 <- sdtm_xx3 %>%
  
  # XXBLFL
  create_BLFL(sort_date = "XXDTC",
              domain = domain,
              grouping_vars = c("USUBJID", "XXTESTCD")) %>%
  
  # EPOCH
  create_EPOCH(date_col = "XXDTC") %>%
  
  # XXDY
  calc_DY(DY_col = "XXDY", DTC_col = "XXDTC")
  
# check the new variables and their related columns only
sdtm_xx4 %>%
  select(USUBJID, XXTEST, XXORRES, XXDTC, XXBLFL, 
         EPOCH, XXDY, starts_with("RF")) %>%
  knitr::kable()

USUBJID	XXTEST	XXORRES	XXDTC	XXBLFL	EPOCH	XXDY	RFSTDTC	RFXSTDTC	RFXENDTC
Subject 1	Test 1	1	2023-08-01	NA	SCREENING	-1	2023-08-02	2023-08-02	2023-08-03
Subject 1	Test 1	0	2023-08-02	Y	TREATMENT	1	2023-08-02	2023-08-02	2023-08-03
Subject 1	Test 1	2	2023-08-03	NA	TREATMENT	2	2023-08-02	2023-08-02	2023-08-03
Subject 1	Test 2	100	2023-08-03	NA	TREATMENT	2	2023-08-02	2023-08-02	2023-08-03
Subject 1	Test 3	PASS	2023-08-04	NA	FOLLOW-UP	3	2023-08-02	2023-08-02	2023-08-03
Subject 2	Test 1	1	2023-08-02	Y	SCREENING	-1	2023-08-03	2023-08-03	2023-08-04
Subject 2	Test 1	NA	2023-08-03	NA	TREATMENT	1	2023-08-03	2023-08-03	2023-08-04
Subject 2	Test 1	2	2023-08-04	NA	TREATMENT	2	2023-08-03	2023-08-03	2023-08-04
Subject 2	Test 2	200	2023-08-04	NA	TREATMENT	2	2023-08-03	2023-08-03	2023-08-04
Subject 2	Test 3	FAIL	2023-08-05	NA	FOLLOW-UP	3	2023-08-03	2023-08-03	2023-08-04

Next, we will assign the sequence number using assign_SEQ() (which also sorts your data frame).

sdtm_xx5 <- assign_SEQ(sdtm_xx4, 
                       key_vars = c("USUBJID", "XXTESTCD", "VISIT"),
                       seq_prefix = domain)

# check the new variable
sdtm_xx5 %>%
  select(USUBJID, XXTESTCD, VISIT, XXDTC, XXSEQ) %>%
  knitr::kable()

USUBJID	XXTESTCD	VISIT	XXDTC	XXSEQ
Subject 1	T1	Visit 1	2023-08-01	1
Subject 1	T1	Visit 2	2023-08-02	2
Subject 1	T1	Visit 3	2023-08-03	3
Subject 1	T2	Visit 3	2023-08-03	4
Subject 1	T3	Visit 4	2023-08-04	5
Subject 2	T1	Visit 1	2023-08-02	1
Subject 2	T1	Visit 2	2023-08-03	2
Subject 2	T1	Visit 3	2023-08-04	3
Subject 2	T2	Visit 3	2023-08-04	4
Subject 2	T3	Visit 4	2023-08-05	5

Now that the bulk of the data cleaning is complete, we will convert all date columns to character columns and all NA values to "" so that our validation table matches the production table produced in SAS. To do this, we will use format_chars_and_dates().

sdtm_xx6 <- format_chars_and_dates(sdtm_xx5)

As a final step, we will assign the meta data from the spec to each column using assign_meta_data(). The meta data includes the labels for each column and their maximum allowed character lengths.

sdtm_xx7 <- sdtm_xx6 %>%
  
  # only keep columns that are domain variables and order them per the spec
  select(any_of(spec$Variable)) %>%
  
  # assign variable lengths and labels
  assign_meta_data(spec = spec)

# show the final SDTM domain
knitr::kable(sdtm_xx7)

STUDYID	USUBJID	XXSEQ	XXTESTCD	XXTEST	XXORRES	XXBLFL	VISIT	EPOCH	XXDTC	XXDY
Study 1	Subject 1	1	T1	Test 1	1		Visit 1	SCREENING	2023-08-01	-1
Study 1	Subject 1	2	T1	Test 1	0	Y	Visit 2	TREATMENT	2023-08-02	1
Study 1	Subject 1	3	T1	Test 1	2		Visit 3	TREATMENT	2023-08-03	2
Study 1	Subject 1	4	T2	Test 2	100		Visit 3	TREATMENT	2023-08-03	2
Study 1	Subject 1	5	T3	Test 3	PASS		Visit 4	FOLLOW-UP	2023-08-04	3
Study 1	Subject 2	1	T1	Test 1	1	Y	Visit 1	SCREENING	2023-08-02	-1
Study 1	Subject 2	2	T1	Test 1			Visit 2	TREATMENT	2023-08-03	1
Study 1	Subject 2	3	T1	Test 1	2		Visit 3	TREATMENT	2023-08-04	2
Study 1	Subject 2	4	T2	Test 2	200		Visit 3	TREATMENT	2023-08-04	2
Study 1	Subject 2	5	T3	Test 3	FAIL		Visit 4	FOLLOW-UP	2023-08-05	3

# check the meta data was assigned
labels <- colnames(sdtm_xx7) %>%
  purrr::map(~ attr(sdtm_xx7[[.]], "label")) %>%
  unlist()
lengths <- colnames(sdtm_xx7) %>%
  purrr::map(~ attr(sdtm_xx7[[.]], "width")) %>%
  unlist()
data.frame(
  column = colnames(sdtm_xx7),
  labels = labels,
  lengths = lengths
)
#>      column                              labels lengths
#> 1   STUDYID                    Study Identifier     200
#> 2   USUBJID           Unique Subject Identifier     200
#> 3     XXSEQ                     Sequence Number       8
#> 4  XXTESTCD                  XX Test Short Name       8
#> 5    XXTEST                        XX Test Name      40
#> 6   XXORRES Result or Finding in Original Units     200
#> 7    XXBLFL                       Baseline Flag       1
#> 8     VISIT                          Visit Name     200
#> 9     EPOCH                               Epoch     200
#> 10    XXDTC           Date/Time of Measurements      19
#> 11     XXDY                     Study Day of XX       8

Finally, we will write the SDTM XX domain validation table as a SAS transport file using write_tbl_to_xpt().

write_tbl_to_xpt(sdtm_xx7, filename = domain, dir = work_dir)

For each previous steps, we viewed the interim results to demonstrate the features of {sdtmval} however, {sdtmval} is designed to be used with pipe operators so that you can have one long, readable pipe. To demonstrate, we will reproduce the same results from above in one code chunk.

sdtm_xx <- edc_dat$xx %>%
  
  # pre-processing
  trim_and_make_blanks_NA() %>%
  
  # XXTEST
  dplyr::mutate(XXTEST = dplyr::recode(XXTESTCD, !!!xxtestcd_codelist)) %>%

  # get the VISITDTC column from the EDC VD form
  dplyr::left_join(edc_dat$vd, by = c("USUBJID", "VISIT")) %>%
  
  # XXDTC
  dplyr::rename(XXDTC = VISITDTC) %>%

  # get the study start/end dates by subject (RFSTDTC, RFXSTDTC, RFXENDTC)
  dplyr::left_join(sdtm_dat$dm, by = "USUBJID") %>%

  # XXBLFL
  create_BLFL(sort_date = "XXDTC",
              domain = domain,
              grouping_vars = c("USUBJID", "XXTESTCD")) %>%

  # EPOCH
  create_EPOCH(date_col = "XXDTC") %>%

  # XXDY
  calc_DY(DY_col = "XXDY", DTC_col = "XXDTC") %>%

  # XXSEQ
  assign_SEQ(key_vars = c("USUBJID", "XXTESTCD", "VISIT"),
             seq_prefix = domain) %>%

  # final formatting
  format_chars_and_dates() %>%
  dplyr::select(dplyr::any_of(spec$Variable)) %>%
  assign_meta_data(spec = spec)

# check if the two data frames are identical
identical(sdtm_xx, sdtm_xx7)
#> [1] TRUE