The main idea of admiral is that an ADaM dataset is built by a sequence of derivations. Each derivation adds one or more variables or records to the processed dataset. This modular approach makes it easy to adjust code by adding, removing, or modifying derivations. Each derivation is a function call.
In this vignette we will explore some of the different type of derivation functions offered by admiral, as well as the argument conventions they follow and how best to start an admiral script.
To help with the examples showcased, we will load some packages and set up some example data to manipulate later. The dplyr, lubridate and stringr packages are tidyverse packages and are used heavily throughout this vignette. The admiral package also leverages the pharmaversesdtm package for example SDTM datasets which are from the CDISC Pilot Study.
# Uncomment line below if you need to install these packages
# install.packages(c("dplyr", "lubridate", "stringr", "tibble", "pharmaversesdtm", "admiral"))
library(dplyr, warn.conflicts = FALSE)
library(lubridate)
library(stringr)
library(tibble)
library(pharmaversesdtm)
library(admiral)
# Read in SDTM datasets
dm <- pharmaversesdtm::dm
ds <- pharmaversesdtm::ds
ex <- pharmaversesdtm::ex
vs <- pharmaversesdtm::vs
admiral_adsl <- admiral::admiral_adslThe adsl and advs objects will now be prepared to showcase addition of variables and addition of records later.
ex_ext <- ex %>%
derive_vars_dtm(
dtc = EXSTDTC,
new_vars_prefix = "EXST"
)
#> The default value of `ignore_seconds_flag` will change to "TRUE" in admiral
#> 1.4.0.
vs <- vs %>%
filter(
USUBJID %in% c(
"01-701-1015", "01-701-1023", "01-703-1086",
"01-703-1096", "01-707-1037", "01-716-1024"
) &
VSTESTCD %in% c("SYSBP", "DIABP") &
VSPOS == "SUPINE"
)
adsl <- admiral_adsl %>%
select(-TRTSDTM, -TRTSTMF)
advs <- vs %>%
mutate(
PARAM = VSTEST, PARAMCD = VSTESTCD, AVAL = VSSTRESN, AVALU = VSORRESU,
AVISIT = VISIT, AVISITN = VISITNUM
)Note: In the example above, we are reading in dummy R data from pharmaversesdtm. However, if you are using SAS datasets as a starting point, be sure to consult the section Handling of Missing Values in the Programming Concepts and Conventions User Guide to learn how and why you should use the function convert_blanks_to_na() during this process.
The most important functions in admiral are the derivations. Derivations add variables or observations/records to the input dataset. Existing variables and observations of the input dataset are not changed. Derivation functions start with derive_. The first argument of these functions expects the input dataset. This allows us to string together derivations using the %>% operator.
Functions which derive a dedicated variable start with derive_var_ followed by the variable name, e.g., derive_var_trtdurd() derives the TRTDURD variable.
Functions which can derive multiple variables start with derive_vars_ followed by the variable name, e.g., derive_vars_dtm() can derive both the TRTSDTM and TRTSTMF variables.
Functions which derive a dedicated parameter start with derive_param_ followed by the parameter name, e.g., derive_param_bmi() derives the BMI parameter.
Below we can see an example call to one of the most common derivation functions, derive_vars_merged(). This function adds variable(s) to the input dataset based on the contents of another dataset. In this example, we add the treatment start datetime and corresponding imputation flag (EXSTTMF) to adsl by identifying the first record in ex with a non-missing Exposure Start Datetime (EXSTDTM) when sorting by EXSTDM and EXSEQ.
Another common derivation function is derive_param_computed(). This function adds a derived parameter to an input dataset. In the example below, we use it to derive the Mean Arterial Pressure (MAP) from the Systolic and Diastolic values of the blood pressure. The parameters that are needed for the derivation are specified in the parameters argument, and within set_values_to we set all the variable values for the new derived record.
advs <- advs %>%
derive_param_computed(
by_vars = exprs(USUBJID, AVISIT, AVISITN),
parameters = c("SYSBP", "DIABP"),
set_values_to = exprs(
AVAL = (AVAL.SYSBP + 2 * AVAL.DIABP) / 3,
PARAMCD = "MAP",
PARAM = "Mean Arterial Pressure (mmHg)",
AVALU = "mmHg"
)
)Note: For the users’ convenience, admiral actually provides derive_param_map() (wrapper of derive_param_computed()) to derive MAP. The above example serves for illustrative purposes only.
Along with the derivation functions, admiral provides a large arsenal of functions to help you with your ADaM derivations. Here are some of the other categories.
Higher order functions are admiral functions that take other functions as input. They enhance the existing portfolio of derivation functions by allowing greater customization of the latter’s behavior. This is done by allowing a derivation function to be:
call_derivation() - Called multiple times, while varying some of the input arguments.restrict_derivation() - Executed on a subset of the input dataset.slice_derivation() - Executed differently on subsets of the input dataset.Higher order functions are a relatively advanced topic within admiral; you can can read all about them in the dedicated vignette about Higher Order Functions.
Computations expect vectors as input and return a vector. Usually these computation functions can not be used with %>%. These functions can be used in expressions like convert_dtc_to_dt() in the derivation of the Final Lab Visit Date (FINLABDT) in the example below:
# Add the date of the final lab visit to ADSL
adsl <- dm %>%
derive_vars_merged(
dataset_add = ds,
by_vars = exprs(USUBJID),
new_vars = exprs(FINLABDT = convert_dtc_to_dt(DSSTDTC)),
filter_add = DSDECOD == "FINAL LAB VISIT"
)Computations can also be used inside a mutate() statement (or similar), so they can be leveraged when your variable derivations do not require calls to specific admiral functions:
adsl <- adsl %>%
mutate(RFSTDT = convert_dtc_to_dt(RFSTDTC))Filter functions are admiral utilities that filter the input dataset in different manners, for instance returning records that fit/don’t fit a certain condition or that are the first/last observation in a by group. These functions form an important internal backbone to some of the admiral functions, but can also be used on their own to explore or manipulate a dataset. For instance, in the example below we use filter_extreme() to extract the most recent MAP records in advs:
Within the admiral package, any arguments which expect variable names or expressions of variable names, symbols or expressions must be specified rather than strings.
For arguments which expect a single variable name, the name can be specified without quotes and quotation, e.g. new_var = TEMPBL
For arguments which expect one or more variable names, a list of symbols is expected, e.g. by_vars = exprs(PARAMCD, AVISIT)
For arguments which expect a single expression, the expression needs to be passed “as is”, e.g. filter = PARAMCD == "TEMP"
For arguments which expect one or more expressions, a list of expressions is expected, e.g. order = exprs(AVISIT, desc(AESEV))
If you are new to expressions, consider reading the Expressions in Scripts section of our Concepts and Conventions User Guide to learn more.
For the ADaM data structures, an overview of the flow and example function calls for the most common steps are provided by the following vignettes:
admiral also provides template R scripts as a starting point. They can be created by calling use_ad_template(), e.g.,
use_ad_template(
adam_name = "adsl",
save_path = "./ad_adsl.R"
)A list of all available templates can be obtained by list_all_templates():
library(admiral)
list_all_templates()
#> Existing ADaM templates in package 'admiral':
#> • ADAE
#> • ADCM
#> • ADEG
#> • ADEX
#> • ADLB
#> • ADLBHY
#> • ADMH
#> • ADPC
#> • ADPP
#> • ADPPK
#> • ADSL
#> • ADVSSupport is provided via the admiral Slack channel. Additionally, please feel free to raise issues in our GitHub repository.