Tabulate Biomarker Effects on Survival by Subgroup
Source:R/survival_biomarkers_subgroups.R
survival_biomarkers_subgroups.Rd![[Stable]](figures/lifecycle-stable.svg)
Tabulate the estimated effects of multiple continuous biomarker variables across population subgroups.
Usage
extract_survival_biomarkers(
variables,
data,
groups_lists = list(),
control = control_coxreg(),
label_all = "All Patients"
)
tabulate_survival_biomarkers(
df,
vars = c("n_tot", "n_tot_events", "median", "hr", "ci", "pval"),
time_unit = NULL
)Arguments
- variables
(named
listofstring)
list of additional analysis variables.- data
(
data.frame)
the dataset containing the variables to summarize.- groups_lists
(named
listoflist)
optionally contains for eachsubgroupsvariable 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)
a list of parameters as returned by the helper functioncontrol_coxreg().- label_all
(
string)
label for the total population analysis.- df
(
data.frame)
containing all analysis variables, as returned byextract_survival_biomarkers().- vars
(
character)
the name of statistics to be reported amongn_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) andpval(p value of the effect). Note, one of the statisticsn_totandn_tot_events, as well as bothhrandciare required.- time_unit
(
string)
label with unit of median survival time. DefaultNULLskips 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
extract_survival_biomarkers(): 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 across population subgroups in a single data frame.variablescorresponds to the names of variables found indata, passed as a named list and requires elementstte,is_event,biomarkers(vector of continuous biomarker variables) and optionallysubgroupsandstrat.groups_listsoptionally specifies groupings forsubgroupsvariables.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_coxreg_mult_cont_df() which is used internally.
h_tab_surv_one_biomarker() which is used internally.
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") %>%
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
# Typical analysis of two continuous biomarkers `BMRKR1` and `AGE`,
# in multiple regression models containing one covariate `RACE`,
# as well as one stratification variable `STRATA1`. The subgroups
# are defined by the levels of `BMRKR2`.
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 1 400 282 680.9598 0.9838045
#> 2 AGE Age 400 282 680.9598 1.0060610
#> 3 BMRKR1 Continuous Level Biomarker 1 135 95 647.7467 1.0155961
#> 4 AGE Age 135 95 647.7467 1.0095516
#> 5 BMRKR1 Continuous Level Biomarker 1 135 93 646.4069 0.9800800
#> 6 AGE Age 135 93 646.4069 1.0228066
#> 7 BMRKR1 Continuous Level Biomarker 1 130 94 761.2290 0.9437920
#> 8 AGE Age 130 94 761.2290 1.0035277
#> lcl ucl conf_level pval pval_label subgroup var
#> 1 0.9500735 1.018733 0.95 0.35898997 p-value (Wald) All Patients ALL
#> 2 0.9908739 1.021481 0.95 0.43619694 p-value (Wald) All Patients ALL
#> 3 0.9553432 1.079649 0.95 0.61993701 p-value (Wald) LOW BMRKR2
#> 4 0.9801032 1.039885 0.95 0.52910110 p-value (Wald) LOW BMRKR2
#> 5 0.9237871 1.039803 0.95 0.50496922 p-value (Wald) MEDIUM BMRKR2
#> 6 0.9940130 1.052434 0.95 0.12167059 p-value (Wald) MEDIUM BMRKR2
#> 7 0.8822742 1.009599 0.95 0.09253618 p-value (Wald) HIGH BMRKR2
#> 8 0.9776074 1.030135 0.95 0.79197278 p-value (Wald) HIGH BMRKR2
#> var_label row_type
#> 1 All Patients content
#> 2 All Patients content
#> 3 Categorical Level Biomarker 2 analysis
#> 4 Categorical Level Biomarker 2 analysis
#> 5 Categorical Level Biomarker 2 analysis
#> 6 Categorical Level Biomarker 2 analysis
#> 7 Categorical Level Biomarker 2 analysis
#> 8 Categorical Level Biomarker 2 analysis
# Here we group the levels of `BMRKR2` manually.
df_grouped <- extract_survival_biomarkers(
variables = list(
tte = "AVAL",
is_event = "is_event",
biomarkers = c("BMRKR1", "AGE"),
strata = "STRATA1",
covariates = "SEX",
subgroups = "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
#> biomarker biomarker_label n_tot n_tot_events median hr
#> 1 BMRKR1 Continuous Level Biomarker 1 400 282 680.9598 0.9838045
#> 2 AGE Age 400 282 680.9598 1.0060610
#> 3 BMRKR1 Continuous Level Biomarker 1 135 95 647.7467 1.0155961
#> 4 AGE Age 135 95 647.7467 1.0095516
#> 5 BMRKR1 Continuous Level Biomarker 1 270 188 647.7467 0.9981993
#> 6 AGE Age 270 188 647.7467 1.0131282
#> 7 BMRKR1 Continuous Level Biomarker 1 400 282 680.9598 0.9838045
#> 8 AGE Age 400 282 680.9598 1.0060610
#> lcl ucl conf_level pval pval_label subgroup var
#> 1 0.9500735 1.018733 0.95 0.3589900 p-value (Wald) All Patients ALL
#> 2 0.9908739 1.021481 0.95 0.4361969 p-value (Wald) All Patients ALL
#> 3 0.9553432 1.079649 0.95 0.6199370 p-value (Wald) low BMRKR2
#> 4 0.9801032 1.039885 0.95 0.5291011 p-value (Wald) low BMRKR2
#> 5 0.9572619 1.040887 0.95 0.9327722 p-value (Wald) low/medium BMRKR2
#> 6 0.9927059 1.033971 0.95 0.2093518 p-value (Wald) low/medium BMRKR2
#> 7 0.9500735 1.018733 0.95 0.3589900 p-value (Wald) low/medium/high BMRKR2
#> 8 0.9908739 1.021481 0.95 0.4361969 p-value (Wald) low/medium/high BMRKR2
#> var_label row_type
#> 1 All Patients content
#> 2 All Patients content
#> 3 Categorical Level Biomarker 2 analysis
#> 4 Categorical Level Biomarker 2 analysis
#> 5 Categorical Level Biomarker 2 analysis
#> 6 Categorical Level Biomarker 2 analysis
#> 7 Categorical Level Biomarker 2 analysis
#> 8 Categorical Level Biomarker 2 analysis
## Table with default columns.
# df <- <needs_to_be_inputted>
tabulate_survival_biomarkers(df)
#> Total n Total Events Median Hazard Ratio 95% Wald CI p-value (Wald)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Age
#> All Patients 400 282 681.0 1.01 (0.99, 1.02) 0.4362
#> Categorical Level Biomarker 2
#> LOW 135 95 647.7 1.01 (0.98, 1.04) 0.5291
#> MEDIUM 135 93 646.4 1.02 (0.99, 1.05) 0.1217
#> HIGH 130 94 761.2 1.00 (0.98, 1.03) 0.7920
#> Continuous Level Biomarker 1
#> All Patients 400 282 681.0 0.98 (0.95, 1.02) 0.3590
#> Categorical Level Biomarker 2
#> LOW 135 95 647.7 1.02 (0.96, 1.08) 0.6199
#> MEDIUM 135 93 646.4 0.98 (0.92, 1.04) 0.5050
#> HIGH 130 94 761.2 0.94 (0.88, 1.01) 0.0925
## 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))
}