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Add an Aggregated Parameter and Derive the Associated Start and End Dates

Source: R/derive_param_exposure.R
derive_param_exposure.Rd

Add a record computed from the aggregated analysis value of another parameter and compute the start (ASTDT(M))and end date (AENDT(M)) as the minimum and maximum date by by_vars.

Usage

derive_param_exposure(
  dataset = NULL,
  dataset_add,
  by_vars,
  input_code,
  analysis_var,
  summary_fun,
  filter = NULL,
  filter_add = NULL,
  set_values_to = NULL
)

Arguments

dataset

Input dataset

The variables specified by the by_vars argument are expected to be in the dataset.

dataset_add

Additional dataset

The variables specified for by_vars, analysis_var, PARAMCD, alongside either ASTDTM and AENDTM or ASTDT and AENDT are also expected. Observations from the specified dataset are going to be used to calculate and added as new records to the input dataset (dataset).

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)

input_code

Required parameter code

The observations where PARAMCD equals the specified value are considered to compute the summary record.

Permitted Values: A character of PARAMCD value

analysis_var

Analysis variable.

summary_fun

Function that takes as an input the analysis_var and performs the calculation. This can include built-in functions as well as user defined functions, for example mean or function(x) mean(x, na.rm = TRUE).

filter

[Deprecated] Please use filter_add instead.

Filter condition as logical expression to apply during summary calculation. By default, filtering expressions are computed within by_vars as this will help when an aggregating, lagging, or ranking function is involved.

For example,

  • filter = (AVAL > mean(AVAL, na.rm = TRUE)) will filter all AVAL values greater than mean of AVAL with in by_vars.

  • filter = (dplyr::n() > 2) will filter n count of by_vars greater than 2.

filter_add

Filter condition as logical expression to apply during summary calculation. By default, filtering expressions are computed within by_vars as this will help when an aggregating, lagging, or ranking function is involved.

For example,

  • filter_add = (AVAL > mean(AVAL, na.rm = TRUE)) will filter all AVAL values greater than mean of AVAL with in by_vars.

  • filter_add = (dplyr::n() > 2) will filter n count of by_vars greater than 2.

set_values_to

Variable-value pairs

Set a list of variables to some specified value for the new observation(s)

  • LHS refer to a variable. It is expected that at least PARAMCD is defined.

  • RHS refers to the values to set to the variable. This can be a string, a symbol, a numeric value, NA, or an expression. (e.g. exprs(PARAMCD = "TDOSE",PARCAT1 = "OVERALL")).

Permitted Values: List of variable-value pairs

Value

The input dataset with a new record added for each group (with respect to the variables specified for the by_vars parameter). That is, a variable will only be populated in this new record if it is specified in by_vars. For each new record,

  • the variable specified analysis_var is computed as defined by summary_fun,

  • the variable(s) specified on the LHS of set_values_to are set to their paired value (RHS). In addition, the start and end date are computed as the minimum/maximum dates by by_vars.

If the input datasets contains

  • both AxxDTM and AxxDT then all ASTDTM,AENDTM, ASTDT, AENDT are computed

  • only AxxDTM then ASTDTM,AENDTM are computed

  • only AxxDT then ASTDT,AENDT are computed.

Details

For each group (with respect to the variables specified for the by_vars parameter), an observation is added to the output dataset and the defined values are set to the defined variables

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_computed(), derive_param_doseint(), derive_param_exist_flag(), derive_param_framingham(), derive_param_map(), derive_param_qtc(), derive_param_rr(), derive_param_wbc_abs(), derive_summary_records()

Examples

library(tibble)
library(dplyr, warn.conflicts = FALSE)
library(lubridate, warn.conflicts = FALSE)
library(stringr, warn.conflicts = FALSE)
adex <- tribble(
  ~USUBJID, ~PARAMCD, ~AVAL, ~AVALC, ~VISIT, ~ASTDT, ~AENDT,
  "1015", "DOSE", 80, NA_character_, "BASELINE", ymd("2014-01-02"), ymd("2014-01-16"),
  "1015", "DOSE", 85, NA_character_, "WEEK 2", ymd("2014-01-17"), ymd("2014-06-18"),
  "1015", "DOSE", 82, NA_character_, "WEEK 24", ymd("2014-06-19"), ymd("2014-07-02"),
  "1015", "ADJ", NA, NA_character_, "BASELINE", ymd("2014-01-02"), ymd("2014-01-16"),
  "1015", "ADJ", NA, NA_character_, "WEEK 2", ymd("2014-01-17"), ymd("2014-06-18"),
  "1015", "ADJ", NA, NA_character_, "WEEK 24", ymd("2014-06-19"), ymd("2014-07-02"),
  "1017", "DOSE", 80, NA_character_, "BASELINE", ymd("2014-01-05"), ymd("2014-01-19"),
  "1017", "DOSE", 50, NA_character_, "WEEK 2", ymd("2014-01-20"), ymd("2014-05-10"),
  "1017", "DOSE", 65, NA_character_, "WEEK 24", ymd("2014-05-10"), ymd("2014-07-02"),
  "1017", "ADJ", NA, NA_character_, "BASELINE", ymd("2014-01-05"), ymd("2014-01-19"),
  "1017", "ADJ", NA, "ADVERSE EVENT", "WEEK 2", ymd("2014-01-20"), ymd("2014-05-10"),
  "1017", "ADJ", NA, NA_character_, "WEEK 24", ymd("2014-05-10"), ymd("2014-07-02")
) %>%
  mutate(ASTDTM = ymd_hms(paste(ASTDT, "00:00:00")), AENDTM = ymd_hms(paste(AENDT, "00:00:00")))

# Cumulative dose
adex %>%
  derive_param_exposure(
    dataset_add = adex,
    by_vars = exprs(USUBJID),
    set_values_to = exprs(
      PARAMCD = "TDOSE",
      PARCAT1 = "OVERALL",
      AVAL = sum(AVAL, na.rm = TRUE)
    ),
    input_code = "DOSE"
  ) %>%
  select(-ASTDTM, -AENDTM)
#> # A tibble: 14 × 8
#>    USUBJID PARAMCD  AVAL AVALC         VISIT    ASTDT      AENDT      PARCAT1
#>    <chr>   <chr>   <dbl> <chr>         <chr>    <date>     <date>     <chr>  
#>  1 1015    DOSE       80 NA            BASELINE 2014-01-02 2014-01-16 NA     
#>  2 1015    DOSE       85 NA            WEEK 2   2014-01-17 2014-06-18 NA     
#>  3 1015    DOSE       82 NA            WEEK 24  2014-06-19 2014-07-02 NA     
#>  4 1015    ADJ        NA NA            BASELINE 2014-01-02 2014-01-16 NA     
#>  5 1015    ADJ        NA NA            WEEK 2   2014-01-17 2014-06-18 NA     
#>  6 1015    ADJ        NA NA            WEEK 24  2014-06-19 2014-07-02 NA     
#>  7 1017    DOSE       80 NA            BASELINE 2014-01-05 2014-01-19 NA     
#>  8 1017    DOSE       50 NA            WEEK 2   2014-01-20 2014-05-10 NA     
#>  9 1017    DOSE       65 NA            WEEK 24  2014-05-10 2014-07-02 NA     
#> 10 1017    ADJ        NA NA            BASELINE 2014-01-05 2014-01-19 NA     
#> 11 1017    ADJ        NA ADVERSE EVENT WEEK 2   2014-01-20 2014-05-10 NA     
#> 12 1017    ADJ        NA NA            WEEK 24  2014-05-10 2014-07-02 NA     
#> 13 1015    TDOSE     247 NA            NA       2014-01-02 2014-07-02 OVERALL
#> 14 1017    TDOSE     195 NA            NA       2014-01-05 2014-07-02 OVERALL

# average dose in w2-24
adex %>%
  derive_param_exposure(
    dataset_add = adex,
    by_vars = exprs(USUBJID),
    filter_add = VISIT %in% c("WEEK 2", "WEEK 24"),
    set_values_to = exprs(
      PARAMCD = "AVDW224",
      PARCAT1 = "WEEK2-24",
      AVAL = mean(AVAL, na.rm = TRUE)
    ),
    input_code = "DOSE"
  ) %>%
  select(-ASTDTM, -AENDTM)
#> # A tibble: 14 × 8
#>    USUBJID PARAMCD  AVAL AVALC         VISIT    ASTDT      AENDT      PARCAT1 
#>    <chr>   <chr>   <dbl> <chr>         <chr>    <date>     <date>     <chr>   
#>  1 1015    DOSE     80   NA            BASELINE 2014-01-02 2014-01-16 NA      
#>  2 1015    DOSE     85   NA            WEEK 2   2014-01-17 2014-06-18 NA      
#>  3 1015    DOSE     82   NA            WEEK 24  2014-06-19 2014-07-02 NA      
#>  4 1015    ADJ      NA   NA            BASELINE 2014-01-02 2014-01-16 NA      
#>  5 1015    ADJ      NA   NA            WEEK 2   2014-01-17 2014-06-18 NA      
#>  6 1015    ADJ      NA   NA            WEEK 24  2014-06-19 2014-07-02 NA      
#>  7 1017    DOSE     80   NA            BASELINE 2014-01-05 2014-01-19 NA      
#>  8 1017    DOSE     50   NA            WEEK 2   2014-01-20 2014-05-10 NA      
#>  9 1017    DOSE     65   NA            WEEK 24  2014-05-10 2014-07-02 NA      
#> 10 1017    ADJ      NA   NA            BASELINE 2014-01-05 2014-01-19 NA      
#> 11 1017    ADJ      NA   ADVERSE EVENT WEEK 2   2014-01-20 2014-05-10 NA      
#> 12 1017    ADJ      NA   NA            WEEK 24  2014-05-10 2014-07-02 NA      
#> 13 1015    AVDW224  83.5 NA            NA       2014-01-17 2014-07-02 WEEK2-24
#> 14 1017    AVDW224  57.5 NA            NA       2014-01-20 2014-07-02 WEEK2-24

# Any dose adjustment?
adex %>%
  derive_param_exposure(
    dataset_add = adex,
    by_vars = exprs(USUBJID),
    set_values_to = exprs(
      PARAMCD = "TADJ",
      PARCAT1 = "OVERALL",
      AVALC = if_else(sum(!is.na(AVALC)) > 0, "Y", NA_character_)
    ),
    input_code = "ADJ"
  ) %>%
  select(-ASTDTM, -AENDTM)
#> # A tibble: 14 × 8
#>    USUBJID PARAMCD  AVAL AVALC         VISIT    ASTDT      AENDT      PARCAT1
#>    <chr>   <chr>   <dbl> <chr>         <chr>    <date>     <date>     <chr>  
#>  1 1015    DOSE       80 NA            BASELINE 2014-01-02 2014-01-16 NA     
#>  2 1015    DOSE       85 NA            WEEK 2   2014-01-17 2014-06-18 NA     
#>  3 1015    DOSE       82 NA            WEEK 24  2014-06-19 2014-07-02 NA     
#>  4 1015    ADJ        NA NA            BASELINE 2014-01-02 2014-01-16 NA     
#>  5 1015    ADJ        NA NA            WEEK 2   2014-01-17 2014-06-18 NA     
#>  6 1015    ADJ        NA NA            WEEK 24  2014-06-19 2014-07-02 NA     
#>  7 1017    DOSE       80 NA            BASELINE 2014-01-05 2014-01-19 NA     
#>  8 1017    DOSE       50 NA            WEEK 2   2014-01-20 2014-05-10 NA     
#>  9 1017    DOSE       65 NA            WEEK 24  2014-05-10 2014-07-02 NA     
#> 10 1017    ADJ        NA NA            BASELINE 2014-01-05 2014-01-19 NA     
#> 11 1017    ADJ        NA ADVERSE EVENT WEEK 2   2014-01-20 2014-05-10 NA     
#> 12 1017    ADJ        NA NA            WEEK 24  2014-05-10 2014-07-02 NA     
#> 13 1015    TADJ       NA NA            NA       2014-01-02 2014-07-02 OVERALL
#> 14 1017    TADJ       NA Y             NA       2014-01-05 2014-07-02 OVERALL