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Prepares Survival Data for Population Subgroups in Data Frames

Source: R/survival_duration_subgroups.R
extract_survival_subgroups.Rd

[Stable]

Prepares estimates of median survival times and treatment hazard ratios for population subgroups in data frames. Simple wrapper for h_survtime_subgroups_df() and h_coxph_subgroups_df(). Result is a list of two data frames: survtime and hr. variables corresponds to the names of variables found in data, passed as a named list and requires elements tte, is_event, arm and optionally subgroups and strat. groups_lists optionally specifies groupings for subgroups variables.

Usage 

extract_survival_subgroups(
  variables,
  data,
  groups_lists = list(),
  control = control_coxph(),
  label_all = "All Patients"
)

Arguments

variables

(named list of string)
list of additional analysis variables.

data

(data.frame)
the dataset containing the variables to summarize.

groups_lists

(named list of list)
optionally contains for each subgroups variable a list, which specifies the new group levels via the names and the levels that belong to it in the character vectors that are elements of the list.

control

(list)
parameters for comparison details, specified by using the helper function control_coxph(). Some possible parameter options are:

  • pval_method (string)
    p-value method for testing hazard ratio = 1. Default method is "log-rank" which comes from survival::survdiff(), can also be set to "wald" or "likelihood" (from survival::coxph()).

  • ties (string)
    specifying the method for tie handling. Default is "efron", can also be set to "breslow" or "exact". See more in survival::coxph()

  • conf_level (proportion)
    confidence level of the interval for HR.

label_all

(string)
label for the total population analysis.

See also

survival_duration_subgroups

Examples 

library(dplyr)
library(forcats)

adtte <- tern_ex_adtte
adtte_labels <- formatters::var_labels(adtte)

adtte_f <- adtte %>%
  filter(
    PARAMCD == "OS",
    ARM %in% c("B: Placebo", "A: Drug X"),
    SEX %in% c("M", "F")
  ) %>%
  mutate(
    # Reorder levels of ARM to display reference arm before treatment arm.
    ARM = droplevels(fct_relevel(ARM, "B: Placebo")),
    SEX = droplevels(SEX),
    AVALU = as.character(AVALU),
    is_event = CNSR == 0
  )
labels <- c(
  "ARM" = adtte_labels[["ARM"]],
  "SEX" = adtte_labels[["SEX"]],
  "AVALU" = adtte_labels[["AVALU"]],
  "is_event" = "Event Flag"
)
formatters::var_labels(adtte_f)[names(labels)] <- labels

df <- extract_survival_subgroups(
  variables = list(
    tte = "AVAL",
    is_event = "is_event",
    arm = "ARM", subgroups = c("SEX", "BMRKR2")
  ),
  data = adtte_f
)
df
#> $survtime
#>           arm  n n_events    median     subgroup    var
#> 1  B: Placebo 73       57  727.8043 All Patients    ALL
#> 2   A: Drug X 69       44  974.6402 All Patients    ALL
#> 3  B: Placebo 40       31  599.1772            F    SEX
#> 4   A: Drug X 38       24 1016.2982            F    SEX
#> 5  B: Placebo 33       26  888.4916            M    SEX
#> 6   A: Drug X 31       20  974.6402            M    SEX
#> 7  B: Placebo 24       21  735.4722          LOW BMRKR2
#> 8   A: Drug X 26       15  974.6402          LOW BMRKR2
#> 9  B: Placebo 23       14  731.8352       MEDIUM BMRKR2
#> 10  A: Drug X 26       17  964.2197       MEDIUM BMRKR2
#> 11 B: Placebo 26       22  654.8245         HIGH BMRKR2
#> 12  A: Drug X 17       12 1016.2982         HIGH BMRKR2
#>                       var_label row_type
#> 1                  All Patients  content
#> 2                  All Patients  content
#> 3                           Sex analysis
#> 4                           Sex analysis
#> 5                           Sex analysis
#> 6                           Sex analysis
#> 7  Continuous Level Biomarker 2 analysis
#> 8  Continuous Level Biomarker 2 analysis
#> 9  Continuous Level Biomarker 2 analysis
#> 10 Continuous Level Biomarker 2 analysis
#> 11 Continuous Level Biomarker 2 analysis
#> 12 Continuous Level Biomarker 2 analysis
#> 
#> $hr
#>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval
#> 1       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511
#> 2        78           55 0.5595391 0.3246658 0.9643271       0.95 0.03411759
#> 3        64           46 0.9102874 0.5032732 1.6464678       0.95 0.75582028
#> 4        50           36 0.7617717 0.3854349 1.5055617       0.95 0.43236030
#> 5        49           31 0.7651261 0.3641277 1.6077269       0.95 0.47860004
#> 6        43           34 0.6662356 0.3257413 1.3626456       0.95 0.26285846
#>           pval_label     subgroup    var                    var_label row_type
#> 1 p-value (log-rank) All Patients    ALL                 All Patients  content
#> 2 p-value (log-rank)            F    SEX                          Sex analysis
#> 3 p-value (log-rank)            M    SEX                          Sex analysis
#> 4 p-value (log-rank)          LOW BMRKR2 Continuous Level Biomarker 2 analysis
#> 5 p-value (log-rank)       MEDIUM BMRKR2 Continuous Level Biomarker 2 analysis
#> 6 p-value (log-rank)         HIGH BMRKR2 Continuous Level Biomarker 2 analysis
#> 

df_grouped <- extract_survival_subgroups(
  variables = list(
    tte = "AVAL",
    is_event = "is_event",
    arm = "ARM", subgroups = c("SEX", "BMRKR2")
  ),
  data = adtte_f,
  groups_lists = list(
    BMRKR2 = list(
      "low" = "LOW",
      "low/medium" = c("LOW", "MEDIUM"),
      "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
    )
  )
)
df_grouped
#> $survtime
#>           arm  n n_events    median        subgroup    var
#> 1  B: Placebo 73       57  727.8043    All Patients    ALL
#> 2   A: Drug X 69       44  974.6402    All Patients    ALL
#> 3  B: Placebo 40       31  599.1772               F    SEX
#> 4   A: Drug X 38       24 1016.2982               F    SEX
#> 5  B: Placebo 33       26  888.4916               M    SEX
#> 6   A: Drug X 31       20  974.6402               M    SEX
#> 7  B: Placebo 24       21  735.4722             low BMRKR2
#> 8   A: Drug X 26       15  974.6402             low BMRKR2
#> 9  B: Placebo 47       35  735.4722      low/medium BMRKR2
#> 10  A: Drug X 52       32  964.2197      low/medium BMRKR2
#> 11 B: Placebo 73       57  727.8043 low/medium/high BMRKR2
#> 12  A: Drug X 69       44  974.6402 low/medium/high BMRKR2
#>                       var_label row_type
#> 1                  All Patients  content
#> 2                  All Patients  content
#> 3                           Sex analysis
#> 4                           Sex analysis
#> 5                           Sex analysis
#> 6                           Sex analysis
#> 7  Continuous Level Biomarker 2 analysis
#> 8  Continuous Level Biomarker 2 analysis
#> 9  Continuous Level Biomarker 2 analysis
#> 10 Continuous Level Biomarker 2 analysis
#> 11 Continuous Level Biomarker 2 analysis
#> 12 Continuous Level Biomarker 2 analysis
#> 
#> $hr
#>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval
#> 1       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511
#> 2        78           55 0.5595391 0.3246658 0.9643271       0.95 0.03411759
#> 3        64           46 0.9102874 0.5032732 1.6464678       0.95 0.75582028
#> 4        50           36 0.7617717 0.3854349 1.5055617       0.95 0.43236030
#> 5        99           67 0.7472958 0.4600419 1.2139136       0.95 0.23764314
#> 6       142          101 0.7108557 0.4779138 1.0573368       0.95 0.09049511
#>           pval_label        subgroup    var                    var_label
#> 1 p-value (log-rank)    All Patients    ALL                 All Patients
#> 2 p-value (log-rank)               F    SEX                          Sex
#> 3 p-value (log-rank)               M    SEX                          Sex
#> 4 p-value (log-rank)             low BMRKR2 Continuous Level Biomarker 2
#> 5 p-value (log-rank)      low/medium BMRKR2 Continuous Level Biomarker 2
#> 6 p-value (log-rank) low/medium/high BMRKR2 Continuous Level Biomarker 2
#>   row_type
#> 1  content
#> 2 analysis
#> 3 analysis
#> 4 analysis
#> 5 analysis
#> 6 analysis
#>