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Adds a Parameter Computed from the Analysis Value of Other Parameters

Source: R/derive_param_computed.R
derive_param_computed.Rd

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. 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
)

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.

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).

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)

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

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.

Permitted Values: List of variable-value pairs

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: a condition

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)

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

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.

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.

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.

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()

Examples

library(tibble)
library(dplyr)
library(lubridate)

# Example 1a: Derive MAP
advs <- tribble(
  ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~AVALU, ~VISIT,
  "01-701-1015", "DIABP", "Diastolic Blood Pressure (mmHg)", 51, "mmHg", "BASELINE",
  "01-701-1015", "DIABP", "Diastolic Blood Pressure (mmHg)", 50, "mmHg", "WEEK 2",
  "01-701-1015", "SYSBP", "Systolic Blood Pressure (mmHg)", 121, "mmHg", "BASELINE",
  "01-701-1015", "SYSBP", "Systolic Blood Pressure (mmHg)", 121, "mmHg", "WEEK 2",
  "01-701-1028", "DIABP", "Diastolic Blood Pressure (mmHg)", 79, "mmHg", "BASELINE",
  "01-701-1028", "DIABP", "Diastolic Blood Pressure (mmHg)", 80, "mmHg", "WEEK 2",
  "01-701-1028", "SYSBP", "Systolic Blood Pressure (mmHg)", 130, "mmHg", "BASELINE",
  "01-701-1028", "SYSBP", "Systolic Blood Pressure (mmHg)", 132, "mmHg", "WEEK 2"
) %>%
  mutate(
    ADT = case_when(
      VISIT == "BASELINE" ~ as.Date("2024-01-10"),
      VISIT == "WEEK 2" ~ as.Date("2024-01-24")
    ),
    ADTF = NA_character_
  )

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
  )
)
#> # A tibble: 12 × 8
#>    USUBJID     PARAMCD PARAM                   AVAL AVALU VISIT ADT        ADTF 
#>    <chr>       <chr>   <chr>                  <dbl> <chr> <chr> <date>     <chr>
#>  1 01-701-1015 DIABP   Diastolic Blood Press…  51   mmHg  BASE… 2024-01-10 NA   
#>  2 01-701-1015 DIABP   Diastolic Blood Press…  50   mmHg  WEEK… 2024-01-24 NA   
#>  3 01-701-1015 SYSBP   Systolic Blood Pressu… 121   mmHg  BASE… 2024-01-10 NA   
#>  4 01-701-1015 SYSBP   Systolic Blood Pressu… 121   mmHg  WEEK… 2024-01-24 NA   
#>  5 01-701-1028 DIABP   Diastolic Blood Press…  79   mmHg  BASE… 2024-01-10 NA   
#>  6 01-701-1028 DIABP   Diastolic Blood Press…  80   mmHg  WEEK… 2024-01-24 NA   
#>  7 01-701-1028 SYSBP   Systolic Blood Pressu… 130   mmHg  BASE… 2024-01-10 NA   
#>  8 01-701-1028 SYSBP   Systolic Blood Pressu… 132   mmHg  WEEK… 2024-01-24 NA   
#>  9 01-701-1015 MAP     Mean Arterial Pressur…  74.3 mmHg  BASE… 2024-01-10 NA   
#> 10 01-701-1015 MAP     Mean Arterial Pressur…  73.7 mmHg  WEEK… 2024-01-24 NA   
#> 11 01-701-1028 MAP     Mean Arterial Pressur…  96   mmHg  BASE… 2024-01-10 NA   
#> 12 01-701-1028 MAP     Mean Arterial Pressur…  97.3 mmHg  WEEK… 2024-01-24 NA   

# Example 1b: Using option `keep_nas = TRUE` to derive MAP in the case where some/all values
# of a variable used in the computation are missing

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
)
#> # A tibble: 12 × 8
#>    USUBJID     PARAMCD PARAM                   AVAL AVALU VISIT ADT        ADTF 
#>    <chr>       <chr>   <chr>                  <dbl> <chr> <chr> <date>     <chr>
#>  1 01-701-1015 DIABP   Diastolic Blood Press…  51   mmHg  BASE… 2024-01-10 NA   
#>  2 01-701-1015 DIABP   Diastolic Blood Press…  50   mmHg  WEEK… 2024-01-24 NA   
#>  3 01-701-1015 SYSBP   Systolic Blood Pressu… 121   mmHg  BASE… 2024-01-10 NA   
#>  4 01-701-1015 SYSBP   Systolic Blood Pressu… 121   mmHg  WEEK… 2024-01-24 NA   
#>  5 01-701-1028 DIABP   Diastolic Blood Press…  79   mmHg  BASE… 2024-01-10 NA   
#>  6 01-701-1028 DIABP   Diastolic Blood Press…  80   mmHg  WEEK… 2024-01-24 NA   
#>  7 01-701-1028 SYSBP   Systolic Blood Pressu… 130   mmHg  BASE… 2024-01-10 NA   
#>  8 01-701-1028 SYSBP   Systolic Blood Pressu… 132   mmHg  WEEK… 2024-01-24 NA   
#>  9 01-701-1015 MAP     Mean Arterial Pressur…  74.3 mmHg  BASE… 2024-01-10 NA   
#> 10 01-701-1015 MAP     Mean Arterial Pressur…  73.7 mmHg  WEEK… 2024-01-24 NA   
#> 11 01-701-1028 MAP     Mean Arterial Pressur…  96   mmHg  BASE… 2024-01-10 NA   
#> 12 01-701-1028 MAP     Mean Arterial Pressur…  97.3 mmHg  WEEK… 2024-01-24 NA   

# Example 2: Derive BMI where height is measured only once
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: Using data from an additional dataset and other variables than AVAL
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", "CHSF213"), 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
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…