Tabulate Survival Duration by Subgroup

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
)

Arguments

.formats: (named character or list)
formats for the statistics.
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) and 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.
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.

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 used to format the results of extract_survival_subgroups(). Returns is a list of Formatted Analysis functions with one element per statistic.
tabulate_survival_subgroups(): table creating function.

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
#> 
# Internal function - a_survival_subgroups
if (FALSE) {
a_survival_subgroups(.formats = list("n" = "xx", "median" = "xx.xx"))
}


## Table with default columns.
basic_table() %>%
  tabulate_survival_subgroups(df, time_unit = adtte_f$AVALU[1])
#> Baseline Risk Factors                               B: Placebo               A: Drug X                                     
#>                                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]
  )
#> Baseline Risk Factors                               B: Placebo               A: Drug X                                                          
#>                                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

Usage

Arguments

Details

Functions

See also

Examples