Skip to contents

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 

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

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

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" that comes 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.

.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.

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

  • extract_survival_subgroups(): 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.

  • 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 


# Testing dataset.
library(scda)
library(dplyr)
library(forcats)
library(rtables)

adtte <- synthetic_cdisc_data("latest")$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 134       87  837.4280 All Patients    ALL
#> 2   A: Drug X 134       79 1260.4905 All Patients    ALL
#> 3  B: Placebo  82       50  850.9208            F    SEX
#> 4   A: Drug X  79       45 1274.8047            F    SEX
#> 5  B: Placebo  52       37  527.6659            M    SEX
#> 6   A: Drug X  55       34  849.2976            M    SEX
#> 7  B: Placebo  45       30  751.4314          LOW BMRKR2
#> 8   A: Drug X  50       31 1160.6458          LOW BMRKR2
#> 9  B: Placebo  56       36  722.7926       MEDIUM BMRKR2
#> 10  A: Drug X  37       19 1269.4039       MEDIUM BMRKR2
#> 11 B: Placebo  33       21  848.2393         HIGH BMRKR2
#> 12  A: Drug X  47       29 1070.8022         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  Categorical Level Biomarker 2 analysis
#> 8  Categorical Level Biomarker 2 analysis
#> 9  Categorical Level Biomarker 2 analysis
#> 10 Categorical Level Biomarker 2 analysis
#> 11 Categorical Level Biomarker 2 analysis
#> 12 Categorical Level Biomarker 2 analysis
#> 
#> $hr
#>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval
#> 1       268          166 0.7173651 0.5275231 0.9755262       0.95 0.03340293
#> 2       161           95 0.6979693 0.4647812 1.0481517       0.95 0.08148174
#> 3       107           71 0.7836167 0.4873444 1.2600023       0.95 0.31318347
#> 4        95           61 0.7050730 0.4243655 1.1714617       0.95 0.17526198
#> 5        93           55 0.5728069 0.3244196 1.0113683       0.95 0.05174942
#> 6        80           50 0.9769002 0.5552002 1.7189005       0.95 0.93538927
#>           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 Categorical Level Biomarker 2 analysis
#> 5 p-value (log-rank)       MEDIUM BMRKR2 Categorical Level Biomarker 2 analysis
#> 6 p-value (log-rank)         HIGH BMRKR2 Categorical 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 134       87  837.4280    All Patients    ALL
#> 2   A: Drug X 134       79 1260.4905    All Patients    ALL
#> 3  B: Placebo  82       50  850.9208               F    SEX
#> 4   A: Drug X  79       45 1274.8047               F    SEX
#> 5  B: Placebo  52       37  527.6659               M    SEX
#> 6   A: Drug X  55       34  849.2976               M    SEX
#> 7  B: Placebo  45       30  751.4314             low BMRKR2
#> 8   A: Drug X  50       31 1160.6458             low BMRKR2
#> 9  B: Placebo 101       66  741.8707      low/medium BMRKR2
#> 10  A: Drug X  87       50 1269.4039      low/medium BMRKR2
#> 11 B: Placebo 134       87  837.4280 low/medium/high BMRKR2
#> 12  A: Drug X 134       79 1260.4905 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  Categorical Level Biomarker 2 analysis
#> 8  Categorical Level Biomarker 2 analysis
#> 9  Categorical Level Biomarker 2 analysis
#> 10 Categorical Level Biomarker 2 analysis
#> 11 Categorical Level Biomarker 2 analysis
#> 12 Categorical Level Biomarker 2 analysis
#> 
#> $hr
#>   arm n_tot n_tot_events        hr       lcl       ucl conf_level       pval
#> 1       268          166 0.7173651 0.5275231 0.9755262       0.95 0.03340293
#> 2       161           95 0.6979693 0.4647812 1.0481517       0.95 0.08148174
#> 3       107           71 0.7836167 0.4873444 1.2600023       0.95 0.31318347
#> 4        95           61 0.7050730 0.4243655 1.1714617       0.95 0.17526198
#> 5       188          116 0.6453648 0.4447544 0.9364622       0.95 0.02019120
#> 6       268          166 0.7173651 0.5275231 0.9755262       0.95 0.03340293
#>           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 Categorical Level Biomarker 2
#> 5 p-value (log-rank)      low/medium BMRKR2 Categorical Level Biomarker 2
#> 6 p-value (log-rank) low/medium/high BMRKR2 Categorical Level Biomarker 2
#>   row_type
#> 1  content
#> 2 analysis
#> 3 analysis
#> 4 analysis
#> 5 analysis
#> 6 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                        166          87         837.4         79        1260.5           0.72       (0.53, 0.98)
#> Sex                                                                                                                         
#>   F                                  95          50         850.9         45        1274.8           0.70       (0.46, 1.05)
#>   M                                  71          37         527.7         34         849.3           0.78       (0.49, 1.26)
#> Categorical Level Biomarker 2                                                                                               
#>   LOW                                61          30         751.4         31        1160.6           0.71       (0.42, 1.17)
#>   MEDIUM                             55          36         722.8         19        1269.4           0.57       (0.32, 1.01)
#>   HIGH                               50          21         848.2         29        1070.8           0.98       (0.56, 1.72)

## 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                        166          87         837.4         79        1260.5           0.72       (0.53, 0.98)         0.0334      
#> Sex                                                                                                                                              
#>   F                                  95          50         850.9         45        1274.8           0.70       (0.46, 1.05)         0.0815      
#>   M                                  71          37         527.7         34         849.3           0.78       (0.49, 1.26)         0.3132      
#> Categorical Level Biomarker 2                                                                                                                    
#>   LOW                                61          30         751.4         31        1160.6           0.71       (0.42, 1.17)         0.1753      
#>   MEDIUM                             55          36         722.8         19        1269.4           0.57       (0.32, 1.01)         0.0517      
#>   HIGH                               50          21         848.2         29        1070.8           0.98       (0.56, 1.72)         0.9354