Skip to contents

Calculate Best RECIST Response

Source: R/calc_best_response.R
calc_best_response.Rd

[Experimental]
Computes the best RECIST response per subject based on target lesion sum and response categories. Ties are resolved using lesion sum, then by date.

Usage

calc_best_response(
  data_recist,
  ...,
  cols = c(rc_sum = "RCTLSUM", rc_resp = "RCRESP", rc_date = "RCDT", subjid = "SUBJID"),
  warnings = getOption("grstat_best_resp_warnings", TRUE),
  confirmed = FALSE,
  cycle_length = 28,
  use_pharmasug = FALSE
)

Arguments

data_recist

A dataset containing longitudinal RECIST data in long format.

...

Not used. Ensures that only named arguments are passed.

cols

a vector with column names inside calc_best_response()

  • subjid The column containing the subject ID. Default is "SUBJID".

  • rc_sum The column containing the sum of target lesions. Default is "RCTLSUM".

  • rc_date The column containing the assessment date. Default is "RCDT".

  • rc_resp The column containing the RECIST response (e.g., "CR", "PR", "SD", "PD"). Default is "RCRESP".

warnings

Logical; if TRUE (default is taken from getOption("grstat_best_resp_warnings", TRUE)), emits warnings during internal checks.

confirmed

Logical; if TRUE, use the cofirmation method to determine the best response. For CR & PR confirmation of response had to be be demonstrated with an assessment 4 weeks or later from the initial response for response.

cycle_length

Numeric, Time between two cycle (used for confirmation), default = 28 days following PharmaSUG 2023 – Paper QT047 recommendation

use_pharmasug

Logical, if TRUE, the confirmation of response will be defnied following PharmaSUG 2023 – Paper QT047 recommendation. Default is RECIST 1.1 guideline

Value

A tibble with one row per subject, containing:

  • subjid: Subject ID

  • best_response: The best RECIST response observed before progression

  • date: The date corresponding to the best response

  • target_sum: Sum of target lesions at that date

  • target_sum_diff_first: Relative difference in target sum compared to baseline

  • target_sum_diff_min: Relative difference in target sum compared to the minimum observed

Details

The function identifies the best response using the following logic:

  1. Responses are ordered: CR > PR > SD > PD > Missing

  2. Among the best responses the earliest assessment date is selected. For a confirmed response, the earliest date of the confirmed response will be selected

  3. Only subjects with at least two assessments and non-missing target sum are considered

Examples

db = grstat_example()
db$recist %>%
  calc_best_response()
#> Warning: Target Lesions Length Sum is missing at baseline. (2 patients: #72 and #193)
#> # A tibble: 200 × 7
#>    subjid best_response       date       target_sum target_sum_diff_first
#>  *  <int> <fct>               <date>          <dbl>                 <dbl>
#>  1      1 Complete response   2023-04-16        0                  -1    
#>  2      2 Progressive disease 2023-04-23       40.6                 1.33 
#>  3      3 Stable disease      2023-05-13       79                   0.122
#>  4      4 Complete response   2023-05-17        0                  -1    
#>  5      5 Complete response   2023-07-05        0                  -1    
#>  6      6 Complete response   2023-08-31        0                  -1    
#>  7      7 Partial response    2023-09-16       50.5                -0.512
#>  8      8 Partial response    2023-08-05       20.3                -0.398
#>  9      9 Partial response    2023-09-03       16                  -0.310
#> 10     10 Complete response   2024-01-11        0                  -1    
#> # ℹ 190 more rows
#> # ℹ 2 more variables: target_sum_diff_min <dbl>, six_months_confirmation <lgl>