Tabulate Biomarker Effects on Survival by Subgroup

Tabulate the estimated effects of multiple continuous biomarker variables across population subgroups.

Usage

tabulate_survival_biomarkers(
  df,
  vars = c("n_tot", "n_tot_events", "median", "hr", "ci", "pval"),
  time_unit = NULL,
  .indent_mods = 0L
)

Arguments

df

(data.frame)
containing all analysis variables, as returned by extract_survival_biomarkers().

vars

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

n_tot_events: Total number of events per group.
n_tot: Total 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.

.indent_mods

(named integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

Value

An rtables table summarizing biomarker effects on survival by subgroup.

Details

These functions create a layout starting from a data frame which contains the required statistics. The tables are then typically used as input for forest plots.

Functions

tabulate_survival_biomarkers(): Table-creating function which creates a table summarizing biomarker effects on survival by subgroup.

Note

In contrast to tabulate_survival_subgroups() this tabulation function does not start from an input layout lyt. This is because internally the table is created by combining multiple subtables.

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") %>%
  mutate(
    AVALU = as.character(AVALU),
    is_event = CNSR == 0
  )
labels <- c("AVALU" = adtte_labels[["AVALU"]], "is_event" = "Event Flag")
formatters::var_labels(adtte_f)[names(labels)] <- labels

df <- extract_survival_biomarkers(
  variables = list(
    tte = "AVAL",
    is_event = "is_event",
    biomarkers = c("BMRKR1", "AGE"),
    strata = "STRATA1",
    covariates = "SEX",
    subgroups = "BMRKR2"
  ),
  data = adtte_f
)
df
#>   biomarker              biomarker_label n_tot n_tot_events   median        hr
#> 1    BMRKR1 Continuous Level Biomarker 1   200          141 753.5176 1.0010939
#> 2       AGE                          Age   200          141 753.5176 1.0106406
#> 3    BMRKR1 Continuous Level Biomarker 1    70           52 735.4722 0.9905065
#> 4       AGE                          Age    70           52 735.4722 1.0106279
#> 5    BMRKR1 Continuous Level Biomarker 1    68           42 858.9952 0.9623210
#> 6       AGE                          Age    68           42 858.9952 1.0360765
#> 7    BMRKR1 Continuous Level Biomarker 1    62           47 727.8043 1.0770946
#> 8       AGE                          Age    62           47 727.8043 1.0009890
#>         lcl      ucl conf_level      pval     pval_label     subgroup    var
#> 1 0.9538978 1.050625       0.95 0.9646086 p-value (Wald) All Patients    ALL
#> 2 0.9871004 1.034742       0.95 0.3787395 p-value (Wald) All Patients    ALL
#> 3 0.9142220 1.073156       0.95 0.8155443 p-value (Wald)          LOW BMRKR2
#> 4 0.9621192 1.061582       0.95 0.6735773 p-value (Wald)          LOW BMRKR2
#> 5 0.8708694 1.063376       0.95 0.4509368 p-value (Wald)       MEDIUM BMRKR2
#> 6 0.9727439 1.103532       0.95 0.2707796 p-value (Wald)       MEDIUM BMRKR2
#> 7 0.9756250 1.189118       0.95 0.1412524 p-value (Wald)         HIGH BMRKR2
#> 8 0.9678535 1.035259       0.95 0.9541048 p-value (Wald)         HIGH BMRKR2
#>                      var_label row_type
#> 1                 All Patients  content
#> 2                 All Patients  content
#> 3 Continuous Level Biomarker 2 analysis
#> 4 Continuous Level Biomarker 2 analysis
#> 5 Continuous Level Biomarker 2 analysis
#> 6 Continuous Level Biomarker 2 analysis
#> 7 Continuous Level Biomarker 2 analysis
#> 8 Continuous Level Biomarker 2 analysis

## Table with default columns.
tabulate_survival_biomarkers(df)
#>                                  Total n   Total Events   Median   Hazard Ratio   95% Wald CI    p-value (Wald)
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Age                                                                                                            
#>   All Patients                     200         141        753.5        1.01       (0.99, 1.03)       0.3787    
#>   Continuous Level Biomarker 2                                                                                 
#>     LOW                            70           52        735.5        1.01       (0.96, 1.06)       0.6736    
#>     MEDIUM                         68           42        859.0        1.04       (0.97, 1.10)       0.2708    
#>     HIGH                           62           47        727.8        1.00       (0.97, 1.04)       0.9541    
#> Continuous Level Biomarker 1                                                                                   
#>   All Patients                     200         141        753.5        1.00       (0.95, 1.05)       0.9646    
#>   Continuous Level Biomarker 2                                                                                 
#>     LOW                            70           52        735.5        0.99       (0.91, 1.07)       0.8155    
#>     MEDIUM                         68           42        859.0        0.96       (0.87, 1.06)       0.4509    
#>     HIGH                           62           47        727.8        1.08       (0.98, 1.19)       0.1413    

## Table with a manually chosen set of columns: leave out "pval", reorder.
tab <- tabulate_survival_biomarkers(
  df = df,
  vars = c("n_tot_events", "ci", "n_tot", "median", "hr"),
  time_unit = as.character(adtte_f$AVALU[1])
)

## Finally produce the forest plot.
if (FALSE) {
g_forest(tab, xlim = c(0.8, 1.2))
}