Helper Functions for Tabulating Survival Duration by Subgroup — h_survival_duration

Helper functions that tabulate in a data frame statistics such as median survival time and hazard ratio for population subgroups.

Usage

h_survtime_df(tte, is_event, arm)

h_survtime_subgroups_df(
  variables,
  data,
  groups_lists = list(),
  label_all = "All Patients"
)

h_coxph_df(tte, is_event, arm, strata_data = NULL, control = control_coxph())

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

Arguments

tte

(numeric)
contains time-to-event duration values.

is_event

(logical)
TRUE if event, FALSE if time to event is censored.

arm

(factor)
the treatment group variable.

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.

label_all

(string)
label for the total population analysis.

strata_data

(factor, data.frame or NULL)
required if stratified analysis is performed.

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.

Details

Main functionality is to prepare data for use in a layout creating function.

Functions

h_survtime_df(): helper to prepare a data frame of median survival times by arm.
h_survtime_subgroups_df(): summarizes median survival times by arm and across subgroups in a data frame. 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. groups_lists optionally specifies groupings for subgroups variables.
h_coxph_df(): helper to prepare a data frame with estimates of treatment hazard ratio.
h_coxph_subgroups_df(): summarizes estimates of the treatment hazard ratio across subgroups in a data frame. 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.

Examples

library(dplyr)
library(forcats)

adtte <- tern_ex_adtte

# Save variable labels before data processing steps.
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),
    is_event = CNSR == 0
  )
labels <- c("ARM" = adtte_labels[["ARM"]], "SEX" = adtte_labels[["SEX"]], "is_event" = "Event Flag")
formatters::var_labels(adtte_f)[names(labels)] <- labels

# Extract median survival time for one group.
h_survtime_df(
  tte = adtte_f$AVAL,
  is_event = adtte_f$is_event,
  arm = adtte_f$ARM
)
#>          arm  n n_events   median
#> 1 B: Placebo 73       57 727.8043
#> 2  A: Drug X 69       44 974.6402

# Extract median survival time for multiple groups.
h_survtime_subgroups_df(
  variables = list(
    tte = "AVAL",
    is_event = "is_event",
    arm = "ARM",
    subgroups = c("SEX", "BMRKR2")
  ),
  data = adtte_f
)
#>           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

# Define groupings for BMRKR2 levels.
h_survtime_subgroups_df(
  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")
    )
  )
)
#>           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

# Extract hazard ratio for one group.
h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM)
#>   arm n_tot n_tot_events        hr       lcl      ucl conf_level       pval
#> 1       142          101 0.7108557 0.4779138 1.057337       0.95 0.09049511
#>           pval_label
#> 1 p-value (log-rank)

# Extract hazard ratio for one group with stratification factor.
h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM, strata_data = adtte_f$STRATA1)
#>   arm n_tot n_tot_events        hr       lcl     ucl conf_level       pval
#> 1       142          101 0.6646586 0.4399495 1.00414       0.95 0.05089188
#>           pval_label
#> 1 p-value (log-rank)

# Extract hazard ratio for multiple groups.
h_coxph_subgroups_df(
  variables = list(
    tte = "AVAL",
    is_event = "is_event",
    arm = "ARM",
    subgroups = c("SEX", "BMRKR2")
  ),
  data = adtte_f
)
#>   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

# Define groupings of BMRKR2 levels.
h_coxph_subgroups_df(
  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")
    )
  )
)
#>   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

# Extract hazard ratio for multiple groups with stratification factors.
h_coxph_subgroups_df(
  variables = list(
    tte = "AVAL",
    is_event = "is_event",
    arm = "ARM",
    subgroups = c("SEX", "BMRKR2"),
    strat = c("STRATA1", "STRATA2")
  ),
  data = adtte_f
)
#>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval
#> 1       142          101 0.6126133 0.3913507 0.9589739       0.95 0.03086774
#> 2        78           55 0.3934024 0.2027682 0.7632630       0.95 0.00469167
#> 3        64           46 0.9501768 0.4730073 1.9087145       0.95 0.88580522
#> 4        50           36 0.7378635 0.3140465 1.7336363       0.95 0.48408079
#> 5        49           31 0.9408062 0.4172095 2.1215148       0.95 0.88305965
#> 6        43           34 0.5125617 0.2125140 1.2362459       0.95 0.13124382
#>           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