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admiral (version 1.3.0)

derive_param_computed: Adds a Parameter Computed from the Analysis Value of Other Parameters

Description

Adds a parameter computed from the analysis value of other parameters. It is expected that the analysis value of the new parameter is defined by an expression using the analysis values of other parameters, such as addition/sum, subtraction/difference, multiplication/product, division/ratio, exponentiation/logarithm, or by formula.

For example mean arterial pressure (MAP) can be derived from systolic (SYSBP) and diastolic blood pressure (DIABP) with the formula $$MAP = \frac{SYSBP + 2 DIABP}{3}$$

Usage

derive_param_computed(
  dataset = NULL,
  dataset_add = NULL,
  by_vars,
  parameters,
  set_values_to,
  filter = NULL,
  constant_by_vars = NULL,
  constant_parameters = NULL,
  keep_nas = FALSE
)

Value

The input dataset with the new parameter added. Note, a variable will only be populated in the new parameter rows if it is specified in by_vars.

Arguments

dataset

Input dataset

The variables specified by the by_vars argument are expected to be in the dataset. PARAMCD is expected as well.

The variable specified by by_vars and PARAMCD must be a unique key of the input dataset after restricting it by the filter condition (filter parameter) and to the parameters specified by parameters.

Permitted values

a dataset, i.e., a data.frame or tibble

Default value

NULL

dataset_add

Additional dataset

The variables specified by the by_vars parameter are expected.

The variable specified by by_vars and PARAMCD must be a unique key of the additional dataset after restricting it to the parameters specified by parameters.

If the argument is specified, the observations of the additional dataset are considered in addition to the observations from the input dataset (dataset restricted by filter).

Permitted values

a dataset, i.e., a data.frame or tibble

Default value

NULL

by_vars

Grouping variables

For each group defined by by_vars an observation is added to the output dataset. Only variables specified in by_vars will be populated in the newly created records.

Permitted values

list of variables created by exprs(), e.g., exprs(USUBJID, VISIT)

Default value

none

parameters

Required parameter codes

It is expected that all parameter codes (PARAMCD) which are required to derive the new parameter are specified for this parameter or the constant_parameters parameter.

If observations should be considered which do not have a parameter code, e.g., if an SDTM dataset is used, temporary parameter codes can be derived by specifying a list of expressions. The name of the element defines the temporary parameter code and the expression the condition for selecting the records. For example parameters = exprs(HGHT = VSTESTCD == "HEIGHT") selects the observations with VSTESTCD == "HEIGHT" from the input data (dataset and dataset_add), sets PARAMCD = "HGHT" for these observations, and adds them to the observations to consider.

Unnamed elements in the list of expressions are considered as parameter codes. For example, parameters = exprs(WEIGHT, HGHT = VSTESTCD == "HEIGHT") uses the parameter code "WEIGHT" and creates a temporary parameter code "HGHT".

Permitted values

A character vector of PARAMCD values or a list of expressions

Default value

none

set_values_to

Variables to be set

The specified variables are set to the specified values for the new observations. The values of variables of the parameters specified by parameters can be accessed using <variable name>.<parameter code>. For example

exprs(
  AVAL = (AVAL.SYSBP + 2 * AVAL.DIABP) / 3,
  PARAMCD = "MAP"
)

defines the analysis value and parameter code for the new parameter.

Variable names in the expression must not contain more than one dot.

Note that dplyr helper functions such as dplyr::starts_with() should be avoided unless the list of variable-value pairs is clearly specified in a statement via the set_values_to argument.

Permitted values

list of named expressions created by a formula using exprs(), e.g., exprs(AVALC = VSSTRESC, AVAL = yn_to_numeric(AVALC))

Default value

none

filter

Filter condition

The specified condition is applied to the input dataset before deriving the new parameter, i.e., only observations fulfilling the condition are taken into account.

Permitted values

an unquoted condition, e.g., AVISIT == "BASELINE"

Default value

NULL

constant_by_vars

By variables for constant parameters

The constant parameters (parameters that are measured only once) are merged to the other parameters using the specified variables. (Refer to Example 2)

Permitted values

list of variables created by exprs(), e.g., exprs(USUBJID, VISIT)

Default value

NULL

constant_parameters

Required constant parameter codes

It is expected that all the parameter codes (PARAMCD) which are required to derive the new parameter and are measured only once are specified here. For example if BMI should be derived and height is measured only once while weight is measured at each visit. Height could be specified in the constant_parameters parameter. (Refer to Example 2)

If observations should be considered which do not have a parameter code, e.g., if an SDTM dataset is used, temporary parameter codes can be derived by specifying a list of expressions. The name of the element defines the temporary parameter code and the expression the condition for selecting the records. For example constant_parameters = exprs(HGHT = VSTESTCD == "HEIGHT") selects the observations with VSTESTCD == "HEIGHT" from the input data (dataset and dataset_add), sets PARAMCD = "HGHT" for these observations, and adds them to the observations to consider.

Unnamed elements in the list of expressions are considered as parameter codes. For example, constant_parameters = exprs(WEIGHT, HGHT = VSTESTCD == "HEIGHT") uses the parameter code "WEIGHT" and creates a temporary parameter code "HGHT".

Permitted values

A character vector of PARAMCD values or a list of expressions

Default value

NULL

keep_nas

Keep observations with NAs

If the argument is set to TRUE, observations are added even if some of the values contributing to the computed value are NA (see Example 1b).

If the argument is set to a list of variables, observations are added even if some of specified variables are NA (see Example 1c).

Permitted values

TRUE, FALSE, or a list of variables created by exprs() e.g. exprs(ADTF, ATMF)

Default value

FALSE

Examples

Example 1 - Data setup

Examples 1a, 1b, and 1c use the following ADVS data.

ADVS <- tribble(
  ~USUBJID,      ~PARAMCD, ~PARAM,                            ~AVAL, ~VISIT,
  "01-701-1015", "DIABP",  "Diastolic Blood Pressure (mmHg)",    51, "BASELINE",
  "01-701-1015", "DIABP",  "Diastolic Blood Pressure (mmHg)",    50, "WEEK 2",
  "01-701-1015", "SYSBP",  "Systolic Blood Pressure (mmHg)",    121, "BASELINE",
  "01-701-1015", "SYSBP",  "Systolic Blood Pressure (mmHg)",    121, "WEEK 2",
  "01-701-1028", "DIABP",  "Diastolic Blood Pressure (mmHg)",    79, "BASELINE",
  "01-701-1028", "DIABP",  "Diastolic Blood Pressure (mmHg)",    80, "WEEK 2",
  "01-701-1028", "SYSBP",  "Systolic Blood Pressure (mmHg)",    130, "BASELINE",
  "01-701-1028", "SYSBP",  "Systolic Blood Pressure (mmHg)",     NA, "WEEK 2"
) %>%
  mutate(
    AVALU = "mmHg",
    ADT = case_when(
      VISIT == "BASELINE" ~ as.Date("2024-01-10"),
      VISIT == "WEEK 2" ~ as.Date("2024-01-24")
    ),
    ADTF = NA_character_
  )

Example 1a - Adding a parameter computed from a formula (parameters, set_values_to)

Derive mean arterial pressure (MAP) from systolic (SYSBP) and diastolic blood pressure (DIABP).

  • Here, for each USUBJID and VISIT group (specified in by_vars), an observation is added to the output dataset when the filtered input dataset (dataset) contains exactly one observation for each parameter code specified for parameters and all contributing values (e.g., AVAL.SYSBP and AVAL.DIABP) are not NA. Indeed, patient 01-701-1028 does not get a "WEEK 2"-derived record as AVAL is NA for their "WEEK 2" systolic blood pressure.

derive_param_computed(
  ADVS,
  by_vars = exprs(USUBJID, VISIT),
  parameters = c("SYSBP", "DIABP"),
  set_values_to = exprs(
    AVAL = (AVAL.SYSBP + 2 * AVAL.DIABP) / 3,
    PARAMCD = "MAP",
    PARAM = "Mean Arterial Pressure (mmHg)",
    AVALU = "mmHg",
    ADT = ADT.SYSBP
  )
) %>%
select(-PARAM)
#> # A tibble: 11 × 7
#>    USUBJID     PARAMCD  AVAL VISIT    AVALU ADT        ADTF 
#>    <chr>       <chr>   <dbl> <chr>    <chr> <date>     <chr>
#>  1 01-701-1015 DIABP    51   BASELINE mmHg  2024-01-10 <NA> 
#>  2 01-701-1015 DIABP    50   WEEK 2   mmHg  2024-01-24 <NA> 
#>  3 01-701-1015 SYSBP   121   BASELINE mmHg  2024-01-10 <NA> 
#>  4 01-701-1015 SYSBP   121   WEEK 2   mmHg  2024-01-24 <NA> 
#>  5 01-701-1028 DIABP    79   BASELINE mmHg  2024-01-10 <NA> 
#>  6 01-701-1028 DIABP    80   WEEK 2   mmHg  2024-01-24 <NA> 
#>  7 01-701-1028 SYSBP   130   BASELINE mmHg  2024-01-10 <NA> 
#>  8 01-701-1028 SYSBP    NA   WEEK 2   mmHg  2024-01-24 <NA> 
#>  9 01-701-1015 MAP      74.3 BASELINE mmHg  2024-01-10 <NA> 
#> 10 01-701-1015 MAP      73.7 WEEK 2   mmHg  2024-01-24 <NA> 
#> 11 01-701-1028 MAP      96   BASELINE mmHg  2024-01-10 <NA>

Example 1b - Keeping missing values for any source variables (keep_nas = TRUE)

Use option keep_nas = TRUE to derive MAP in the case where some/all values of a variable used in the computation are missing.

  • Note that observations will be added here even if some of the values contributing to the computed values are NA. In particular, patient 01-701-1028 does get a "WEEK 2"-derived record as compared to Example 1a, but with AVAL = NA.

derive_param_computed(
  ADVS,
  by_vars = exprs(USUBJID, VISIT),
  parameters = c("SYSBP", "DIABP"),
  set_values_to = exprs(
    AVAL = (AVAL.SYSBP + 2 * AVAL.DIABP) / 3,
    PARAMCD = "MAP",
    PARAM = "Mean Arterial Pressure (mmHg)",
    AVALU = "mmHg",
    ADT = ADT.SYSBP,
    ADTF = ADTF.SYSBP
  ),
  keep_nas = TRUE
)%>%
select(-PARAM)
#> # A tibble: 12 × 7
#>    USUBJID     PARAMCD  AVAL VISIT    AVALU ADT        ADTF 
#>    <chr>       <chr>   <dbl> <chr>    <chr> <date>     <chr>
#>  1 01-701-1015 DIABP    51   BASELINE mmHg  2024-01-10 <NA> 
#>  2 01-701-1015 DIABP    50   WEEK 2   mmHg  2024-01-24 <NA> 
#>  3 01-701-1015 SYSBP   121   BASELINE mmHg  2024-01-10 <NA> 
#>  4 01-701-1015 SYSBP   121   WEEK 2   mmHg  2024-01-24 <NA> 
#>  5 01-701-1028 DIABP    79   BASELINE mmHg  2024-01-10 <NA> 
#>  6 01-701-1028 DIABP    80   WEEK 2   mmHg  2024-01-24 <NA> 
#>  7 01-701-1028 SYSBP   130   BASELINE mmHg  2024-01-10 <NA> 
#>  8 01-701-1028 SYSBP    NA   WEEK 2   mmHg  2024-01-24 <NA> 
#>  9 01-701-1015 MAP      74.3 BASELINE mmHg  2024-01-10 <NA> 
#> 10 01-701-1015 MAP      73.7 WEEK 2   mmHg  2024-01-24 <NA> 
#> 11 01-701-1028 MAP      96   BASELINE mmHg  2024-01-10 <NA> 
#> 12 01-701-1028 MAP      NA   WEEK 2   mmHg  2024-01-24 <NA>

Example 1c - Keeping missing values for some source variables (keep_nas = exprs())

Use option keep_nas = exprs(ADTF) to derive MAP in the case where some/all values of a variable used in the computation are missing but keeping NA values of ADTF.

  • This is subtly distinct from Examples 1a and 1b. In 1a, we do not get new derived records if any of the source records have a value of NA for a variable that is included in set_values_to. In 1b, we do the opposite and allow the creation of new records regardless of how many NAs we encounter in the source variables.

  • Here, we want to disregard NA values but only from the variables that are specified via keep_na_values.

  • This is important because we have added ADTF in set_values_to, but all values of this variable are NA. As such, in order to get any derived records at all, but continue not getting one when AVAL is NA in any of the source records, (see patient "01-701-1028" again), we specify keep_nas = exprs(ADTF).

derive_param_computed(
  ADVS,
  by_vars = exprs(USUBJID, VISIT),
  parameters = c("SYSBP", "DIABP"),
  set_values_to = exprs(
    AVAL = (AVAL.SYSBP + 2 * AVAL.DIABP) / 3,
    PARAMCD = "MAP",
    PARAM = "Mean Arterial Pressure (mmHg)",
    AVALU = "mmHg",
    ADT = ADT.SYSBP,
    ADTF = ADTF.SYSBP
  ),
  keep_nas = exprs(ADTF)
)
#> # A tibble: 11 × 8
#>    USUBJID     PARAMCD PARAM                   AVAL VISIT AVALU ADT        ADTF 
#>    <chr>       <chr>   <chr>                  <dbl> <chr> <chr> <date>     <chr>
#>  1 01-701-1015 DIABP   Diastolic Blood Press…  51   BASE… mmHg  2024-01-10 <NA> 
#>  2 01-701-1015 DIABP   Diastolic Blood Press…  50   WEEK… mmHg  2024-01-24 <NA> 
#>  3 01-701-1015 SYSBP   Systolic Blood Pressu… 121   BASE… mmHg  2024-01-10 <NA> 
#>  4 01-701-1015 SYSBP   Systolic Blood Pressu… 121   WEEK… mmHg  2024-01-24 <NA> 
#>  5 01-701-1028 DIABP   Diastolic Blood Press…  79   BASE… mmHg  2024-01-10 <NA> 
#>  6 01-701-1028 DIABP   Diastolic Blood Press…  80   WEEK… mmHg  2024-01-24 <NA> 
#>  7 01-701-1028 SYSBP   Systolic Blood Pressu… 130   BASE… mmHg  2024-01-10 <NA> 
#>  8 01-701-1028 SYSBP   Systolic Blood Pressu…  NA   WEEK… mmHg  2024-01-24 <NA> 
#>  9 01-701-1015 MAP     Mean Arterial Pressur…  74.3 BASE… mmHg  2024-01-10 <NA> 
#> 10 01-701-1015 MAP     Mean Arterial Pressur…  73.7 WEEK… mmHg  2024-01-24 <NA> 
#> 11 01-701-1028 MAP     Mean Arterial Pressur…  96   BASE… mmHg  2024-01-10 <NA>

Example 2 - Derivations using parameters measured only once (constant_parameters and constant_by_vars)

Derive BMI where HEIGHT is measured only once.

  • In the above examples, for each parameter specified in the parameters argument, we expect one record per by group, where the by group is specified in by_vars. However, if a parameter is only measured once, it can be specified in constant_parameters instead.

  • A modified by group still needs to be provided for the constant parameters. This can be done via constant_by_vars.

  • See the example below, where weight is measured for each patient at each visit (by_vars = exprs(USUBJID, VISIT)), while height is measured for each patient only at the first visit (constant_parameters = "HEIGHT", constant_by_vars = exprs(USUBJID)).

ADVS <- tribble(
  ~USUBJID,      ~PARAMCD, ~PARAM,        ~AVAL, ~AVALU, ~VISIT,
  "01-701-1015", "HEIGHT", "Height (cm)", 147.0, "cm",   "SCREENING",
  "01-701-1015", "WEIGHT", "Weight (kg)",  54.0, "kg",   "SCREENING",
  "01-701-1015", "WEIGHT", "Weight (kg)",  54.4, "kg",   "BASELINE",
  "01-701-1015", "WEIGHT", "Weight (kg)",  53.1, "kg",   "WEEK 2",
  "01-701-1028", "HEIGHT", "Height (cm)", 163.0, "cm",   "SCREENING",
  "01-701-1028", "WEIGHT", "Weight (kg)",  78.5, "kg",   "SCREENING",
  "01-701-1028", "WEIGHT", "Weight (kg)",  80.3, "kg",   "BASELINE",
  "01-701-1028", "WEIGHT", "Weight (kg)",  80.7, "kg",   "WEEK 2"
)
derive_param_computed(
  ADVS,
  by_vars = exprs(USUBJID, VISIT),
  parameters = "WEIGHT",
  set_values_to = exprs(
    AVAL = AVAL.WEIGHT / (AVAL.HEIGHT / 100)^2,
    PARAMCD = "BMI",
    PARAM = "Body Mass Index (kg/m^2)",
    AVALU = "kg/m^2"
  ),
  constant_parameters = c("HEIGHT"),
  constant_by_vars = exprs(USUBJID)
)
#> # A tibble: 14 × 6
#>    USUBJID     PARAMCD PARAM                     AVAL AVALU  VISIT    
#>    <chr>       <chr>   <chr>                    <dbl> <chr>  <chr>    
#>  1 01-701-1015 HEIGHT  Height (cm)              147   cm     SCREENING
#>  2 01-701-1015 WEIGHT  Weight (kg)               54   kg     SCREENING
#>  3 01-701-1015 WEIGHT  Weight (kg)               54.4 kg     BASELINE 
#>  4 01-701-1015 WEIGHT  Weight (kg)               53.1 kg     WEEK 2   
#>  5 01-701-1028 HEIGHT  Height (cm)              163   cm     SCREENING
#>  6 01-701-1028 WEIGHT  Weight (kg)               78.5 kg     SCREENING
#>  7 01-701-1028 WEIGHT  Weight (kg)               80.3 kg     BASELINE 
#>  8 01-701-1028 WEIGHT  Weight (kg)               80.7 kg     WEEK 2   
#>  9 01-701-1015 BMI     Body Mass Index (kg/m^2)  25.0 kg/m^2 SCREENING
#> 10 01-701-1015 BMI     Body Mass Index (kg/m^2)  25.2 kg/m^2 BASELINE 
#> 11 01-701-1015 BMI     Body Mass Index (kg/m^2)  24.6 kg/m^2 WEEK 2   
#> 12 01-701-1028 BMI     Body Mass Index (kg/m^2)  29.5 kg/m^2 SCREENING
#> 13 01-701-1028 BMI     Body Mass Index (kg/m^2)  30.2 kg/m^2 BASELINE 
#> 14 01-701-1028 BMI     Body Mass Index (kg/m^2)  30.4 kg/m^2 WEEK 2

Example 3 - Derivations including data from an additional dataset (dataset_add) and non-AVAL variables

Use data from an additional dataset and other variables than AVAL.

  • In this example, the dataset specified via dataset_add (e.g., QS) is an SDTM dataset. There is no parameter code in the dataset.

  • The parameters argument is therefore used to specify a list of expressions to derive temporary parameter codes.

  • Then, set_values_to is used to specify the values for the new observations of each variable, and variable-value pairs from both datasets are referenced via exprs().

QS <- tribble(
  ~USUBJID, ~AVISIT,   ~QSTESTCD, ~QSORRES, ~QSSTRESN,
  "1",      "WEEK 2",  "CHSF112", NA,               1,
  "1",      "WEEK 2",  "CHSF113", "Yes",           NA,
  "1",      "WEEK 2",  "CHSF114", NA,               1,
  "1",      "WEEK 4",  "CHSF112", NA,               2,
  "1",      "WEEK 4",  "CHSF113", "No",            NA,
  "1",      "WEEK 4",  "CHSF114", NA,               1
)
ADCHSF <- tribble(
  ~USUBJID, ~AVISIT,  ~PARAMCD, ~QSSTRESN, ~AVAL,
  "1",      "WEEK 2", "CHSF12", 1,             6,
  "1",      "WEEK 2", "CHSF14", 1,             6,
  "1",      "WEEK 4", "CHSF12", 2,            12,
  "1",      "WEEK 4", "CHSF14", 1,             6
) %>%
  mutate(QSORRES = NA_character_)
derive_param_computed(
  ADCHSF,
  dataset_add = QS,
  by_vars = exprs(USUBJID, AVISIT),
  parameters = exprs(CHSF12, CHSF13 = QSTESTCD %in% c("CHSF113"), CHSF14),
  set_values_to = exprs(
    AVAL = case_when(
      QSORRES.CHSF13 == "Not applicable" ~ 0,
      QSORRES.CHSF13 == "Yes" ~ 38,
      QSORRES.CHSF13 == "No" ~ if_else(
        QSSTRESN.CHSF12 > QSSTRESN.CHSF14,
        25,
        0
      )
    ),
    PARAMCD = "CHSF13"
  )
)
#> # A tibble: 6 × 6
#>   USUBJID AVISIT PARAMCD QSSTRESN  AVAL QSORRES
#>   <chr>   <chr>  <chr>      <dbl> <dbl> <chr>  
#> 1 1       WEEK 2 CHSF12         1     6 <NA>   
#> 2 1       WEEK 2 CHSF14         1     6 <NA>   
#> 3 1       WEEK 4 CHSF12         2    12 <NA>   
#> 4 1       WEEK 4 CHSF14         1     6 <NA>   
#> 5 1       WEEK 2 CHSF13        NA    38 <NA>   
#> 6 1       WEEK 4 CHSF13        NA    25 <NA>

Example 4 - Computing more than one variable

Specify more than one variable-value pair via set_values_to.

  • In this example, the values of AVALC, ADTM, ADTF, PARAMCD, and PARAM are determined via distinctly defined analysis values and parameter codes.

  • This is different from Example 3 as more than one variable is derived.

ADLB_TBILIALK <- tribble(
  ~USUBJID, ~PARAMCD, ~AVALC, ~ADTM,        ~ADTF,
  "1",      "ALK2",   "Y",    "2021-05-13", NA_character_,
  "1",      "TBILI2", "Y",    "2021-06-30", "D",
  "2",      "ALK2",   "Y",    "2021-12-31", "M",
  "2",      "TBILI2", "N",    "2021-11-11", NA_character_,
  "3",      "ALK2",   "N",    "2021-04-03", NA_character_,
  "3",      "TBILI2", "N",    "2021-04-04", NA_character_
) %>%
  mutate(ADTM = ymd(ADTM))
derive_param_computed(
  dataset_add = ADLB_TBILIALK,
  by_vars = exprs(USUBJID),
  parameters = c("ALK2", "TBILI2"),
  set_values_to = exprs(
    AVALC = if_else(AVALC.TBILI2 == "Y" & AVALC.ALK2 == "Y", "Y", "N"),
    ADTM = pmax(ADTM.TBILI2, ADTM.ALK2),
    ADTF = if_else(ADTM == ADTM.TBILI2, ADTF.TBILI2, ADTF.ALK2),
    PARAMCD = "TB2AK2",
    PARAM = "TBILI > 2 times ULN and ALKPH <= 2 times ULN"
  ),
  keep_nas = TRUE
)
#> # A tibble: 3 × 6
#>   USUBJID AVALC ADTM       ADTF  PARAMCD PARAM                                  
#>   <chr>   <chr> <date>     <chr> <chr>   <chr>                                  
#> 1 1       Y     2021-06-30 D     TB2AK2  TBILI > 2 times ULN and ALKPH <= 2 tim…
#> 2 2       N     2021-12-31 M     TB2AK2  TBILI > 2 times ULN and ALKPH <= 2 tim…
#> 3 3       N     2021-04-04 <NA>  TB2AK2  TBILI > 2 times ULN and ALKPH <= 2 tim…

Details

For each group (with respect to the variables specified for the by_vars parameter) an observation is added to the output dataset if the filtered input dataset (dataset) or the additional dataset (dataset_add) contains exactly one observation for each parameter code specified for parameters and all contributing values like AVAL.SYSBP are not NA. The keep_nas can be used to specify variables for which NAs are acceptable. See also Example 1b and 1c.

For the new observations the variables specified for set_values_to are set to the provided values. The values of the other variables of the input dataset are set to NA.

See Also

BDS-Findings Functions for adding Parameters/Records: default_qtc_paramcd(), derive_expected_records(), derive_extreme_event(), derive_extreme_records(), derive_locf_records(), derive_param_bmi(), derive_param_bsa(), derive_param_doseint(), derive_param_exist_flag(), derive_param_exposure(), derive_param_framingham(), derive_param_map(), derive_param_qtc(), derive_param_rr(), derive_param_wbc_abs(), derive_summary_records()