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Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup

Source: R/h_survival_biomarkers_subgroups.R
h_survival_biomarkers_subgroups.Rd

[Stable]

Helper functions which are documented here separately to not confuse the user when reading about the user-facing functions.

Usage 

h_surv_to_coxreg_variables(variables, biomarker)

h_coxreg_mult_cont_df(variables, data, control = control_coxreg())

h_tab_surv_one_biomarker(df, vars, time_unit)

Arguments

variables

(named list of string)
list of additional analysis variables.

biomarker

(string)
the name of the biomarker variable.

data

(data.frame)
the dataset containing the variables to summarize.

control

(list)
a list of parameters as returned by the helper function control_coxreg().

df

(data.frame)
results for a single biomarker, as part of what is returned by extract_survival_biomarkers() (it needs a couple of columns which are added by that high-level function relative to what is returned by h_coxreg_mult_cont_df(), see the example).

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.

Value

  • h_surv_to_coxreg_variables() returns a named list of elements time, event, arm, covariates, and strata.

  • h_coxreg_mult_cont_df() returns a data.frame containing estimates and statistics for the selected biomarkers.

  • h_tab_surv_one_biomarker() returns an rtables table object with the given statistics arranged in columns.

Functions

  • h_surv_to_coxreg_variables(): helps with converting the "survival" function variable list to the "Cox regression" variable list. The reason is that currently there is an inconsistency between the variable names accepted by extract_survival_subgroups() and fit_coxreg_multivar().

  • h_coxreg_mult_cont_df(): prepares estimates for number of events, patients and median survival times, as well as hazard ratio estimates, confidence intervals and p-values, for multiple biomarkers in a given single data set. variables corresponds to names of variables found in data, passed as a named list and requires elements tte, is_event, biomarkers (vector of continuous biomarker variables) and optionally subgroups and strat.

  • h_tab_surv_one_biomarker(): prepares a single sub-table given a df_sub containing the results for a single biomarker.

Examples 

library(dplyr)
library(forcats)

adtte <- tern_ex_adtte

# Save variable labels before data processing steps.
adtte_labels <- formatters::var_labels(adtte, fill = FALSE)

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

# This is how the variable list is converted internally.
h_surv_to_coxreg_variables(
  variables = list(
    tte = "AVAL",
    is_event = "EVNT",
    covariates = c("A", "B"),
    strata = "D"
  ),
  biomarker = "AGE"
)
#> $time
#> [1] "AVAL"
#> 
#> $event
#> [1] "EVNT"
#> 
#> $arm
#> [1] "AGE"
#> 
#> $covariates
#> [1] "A" "B"
#> 
#> $strata
#> [1] "D"
#> 

# For a single population, estimate separately the effects
# of two biomarkers.
df <- h_coxreg_mult_cont_df(
  variables = list(
    tte = "AVAL",
    is_event = "is_event",
    biomarkers = c("BMRKR1", "AGE"),
    covariates = "SEX",
    strata = c("STRATA1", "STRATA2")
  ),
  data = adtte_f
)
df
#>   biomarker              biomarker_label n_tot n_tot_events   median       hr
#> 1    BMRKR1 Continuous Level Biomarker 1   200          141 753.5176 1.000189
#> 2       AGE                          Age   200          141 753.5176 1.008267
#>         lcl      ucl conf_level      pval     pval_label
#> 1 0.9511092 1.051802       0.95 0.9941244 p-value (Wald)
#> 2 0.9845155 1.032591       0.95 0.4984743 p-value (Wald)

# If the data set is empty, still the corresponding rows with missings are returned.
h_coxreg_mult_cont_df(
  variables = list(
    tte = "AVAL",
    is_event = "is_event",
    biomarkers = c("BMRKR1", "AGE"),
    covariates = "REGION1",
    strata = c("STRATA1", "STRATA2")
  ),
  data = adtte_f[NULL, ]
)
#>   biomarker              biomarker_label n_tot n_tot_events median hr lcl ucl
#> 1    BMRKR1 Continuous Level Biomarker 1     0            0     NA NA  NA  NA
#> 2       AGE                          Age     0            0     NA NA  NA  NA
#>   conf_level pval     pval_label
#> 1       0.95   NA p-value (Wald)
#> 2       0.95   NA p-value (Wald)

# Starting from above `df`, zoom in on one biomarker and add required columns.
df1 <- df[1, ]
df1$subgroup <- "All patients"
df1$row_type <- "content"
df1$var <- "ALL"
df1$var_label <- "All patients"
h_tab_surv_one_biomarker(
  df1,
  vars = c("n_tot", "n_tot_events", "median", "hr", "ci", "pval"),
  time_unit = "days"
)
#>                Total n   Total Events   Median (days)   Hazard Ratio   95% Wald CI    p-value (Wald)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————
#> All patients     200         141            753.5           1.00       (0.95, 1.05)       0.9941