Tabulate Survival Duration by Subgroup — survival_duration_subgroups • tern

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

Usage

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)")
)

tabulate_survival_subgroups(
  lyt,
  df,
  vars = c("n_tot_events", "n_events", "median", "hr", "ci"),
  time_unit = NULL,
  na_str = NA_character_
)

Arguments

.formats

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

lyt

(layout)
input layout where analyses will be added to.

df

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

vars

(character)
the name 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.

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.

Value

a_survival_subgroups() returns the corresponding list with formatted rtables::CellValue().

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

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

See also

extract_survival_subgroups()

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),
    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
#> 

## 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 
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> All 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)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> All 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