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Tabulate Biomarker Effects on Survival by Subgroup

Source: R/survival_biomarkers_subgroups.R
survival_biomarkers_subgroups.Rd

[Stable]

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
)

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.

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.

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.

See also

h_tab_surv_one_biomarker() which is used internally, extract_survival_biomarkers().

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))
}