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Tabulate survival duration by subgroup

Source: R/survival_duration_subgroups.R
survival_duration_subgroups.Rd

[Stable]

Tabulate statistics such as median survival time and hazard ratio for population subgroups.

Usage 

tabulate_survival_subgroups(
  lyt,
  df,
  vars = c("n_tot_events", "n_events", "median", "hr", "ci"),
  groups_lists = list(),
  label_all = lifecycle::deprecated(),
  time_unit = NULL,
  na_str = default_na_str()
)

a_survival_subgroups(
  .formats = list(n = "xx", n_events = "xx", n_tot_events = "xx", median = "xx.x", n_tot
    = "xx", hr = list(format_extreme_values(2L)), ci =
    list(format_extreme_values_ci(2L)), pval = "x.xxxx | (<0.0001)"),
  na_str = default_na_str()
)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

df

(list)
list of data frames containing all analysis variables. List should be created using extract_survival_subgroups().

vars

(character)
the names of statistics to be reported among:

  • n_tot_events: Total number of events per group.

  • n_events: Number of events per group.

  • n_tot: Total number of observations per group.

  • n: Number of observations per group.

  • median: Median survival time.

  • hr: Hazard ratio.

  • ci: Confidence interval of hazard ratio.

  • pval: p-value of the effect. Note, one of the statistics n_tot and n_tot_events, as well as both hr and ci are required.

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

[Deprecated]
please assign the label_all parameter within the extract_survival_subgroups() function when creating df.

time_unit

(string)
label with unit of median survival time. Default NULL skips displaying unit.

na_str

(string)
string used to replace all NA or empty values in the output.

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for more information on the "auto" setting.

Value

An rtables table summarizing survival by subgroup.

Details

These functions create a layout starting from a data frame which contains the required statistics. Tables typically used as part of forest plot.

Functions

  • tabulate_survival_subgroups(): Table-creating function which creates a table summarizing survival by subgroup. This function is a wrapper for rtables::analyze_colvars() and rtables::summarize_row_groups().

  • a_survival_subgroups(): Formatted analysis function which is used as afun in tabulate_survival_subgroups().

See also

extract_survival_subgroups()

Examples 

library(dplyr)

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(forcats::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")
  ),
  label_all = "Total Patients",
  data = adtte_f
)
df
#> $survtime
#>           arm  n n_events    median       subgroup    var
#> 1  B: Placebo 73       57  727.8043 Total Patients    ALL
#> 2   A: Drug X 69       44  974.6402 Total 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                Total Patients  content
#> 2                Total 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
#> 1 p-value (log-rank) Total Patients    ALL               Total 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)         MEDIUM BMRKR2 Continuous Level Biomarker 2
#> 6 p-value (log-rank)           HIGH BMRKR2 Continuous Level Biomarker 2
#>   row_type
#> 1  content
#> 2 analysis
#> 3 analysis
#> 4 analysis
#> 5 analysis
#> 6 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
#> 

## Table with default columns.
basic_table() %>%
  tabulate_survival_subgroups(df, time_unit = adtte_f$AVALU[1])
#>                                                     B: Placebo               A: Drug X                                     
#> Baseline Risk Factors          Total Events   Events   Median (DAYS)   Events   Median (DAYS)   Hazard Ratio   95% Wald CI 
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Total Patients                     101          57         727.8         44         974.6           0.71       (0.48, 1.06)
#> Sex                                                                                                                        
#>   F                                 55          31         599.2         24        1016.3           0.56       (0.32, 0.96)
#>   M                                 46          26         888.5         20         974.6           0.91       (0.50, 1.65)
#> Continuous Level Biomarker 2                                                                                               
#>   LOW                               36          21         735.5         15         974.6           0.76       (0.39, 1.51)
#>   MEDIUM                            31          14         731.8         17         964.2           0.77       (0.36, 1.61)
#>   HIGH                              34          22         654.8         12        1016.3           0.67       (0.33, 1.36)

## Table with a manually chosen set of columns: adding "pval".
basic_table() %>%
  tabulate_survival_subgroups(
    df = df,
    vars = c("n_tot_events", "n_events", "median", "hr", "ci", "pval"),
    time_unit = adtte_f$AVALU[1]
  )
#>                                                     B: Placebo               A: Drug X                                                          
#> Baseline Risk Factors          Total Events   Events   Median (DAYS)   Events   Median (DAYS)   Hazard Ratio   95% Wald CI    p-value (log-rank)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Total Patients                     101          57         727.8         44         974.6           0.71       (0.48, 1.06)         0.0905      
#> Sex                                                                                                                                             
#>   F                                 55          31         599.2         24        1016.3           0.56       (0.32, 0.96)         0.0341      
#>   M                                 46          26         888.5         20         974.6           0.91       (0.50, 1.65)         0.7558      
#> Continuous Level Biomarker 2                                                                                                                    
#>   LOW                               36          21         735.5         15         974.6           0.76       (0.39, 1.51)         0.4324      
#>   MEDIUM                            31          14         731.8         17         964.2           0.77       (0.36, 1.61)         0.4786      
#>   HIGH                              34          22         654.8         12        1016.3           0.67       (0.33, 1.36)         0.2629