Prepare response data estimates for multiple biomarkers in a single data frame
Source:R/response_biomarkers_subgroups.R
extract_rsp_biomarkers.Rd![[Stable]](figures/lifecycle-stable.svg)
Prepares estimates for number of responses, patients and overall response rate,
as well as odds ratio estimates, confidence intervals and p-values,
for multiple biomarkers across population subgroups in a single data frame.
variables corresponds to the names of variables found in data, passed as a
named list and requires elements rsp and biomarkers (vector of continuous
biomarker variables) and optionally covariates, subgroups and strata.
groups_lists optionally specifies groupings for subgroups variables.
extract_rsp_biomarkers(
variables,
data,
groups_lists = list(),
control = control_logistic(),
label_all = "All Patients"
)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
(named
list)
controls for the response definition and the confidence level produced bycontrol_logistic().- label_all
(
string)
label for the total population analysis.
Value
A data.frame with columns biomarker, biomarker_label, n_tot, n_rsp,
prop, or, lcl, ucl, conf_level, pval, pval_label, subgroup, var,
var_label, and row_type.
Note
You can also specify a continuous variable in rsp and then use the
response_definition control to convert that internally to a logical
variable reflecting binary response.
See also
h_logistic_mult_cont_df() which is used internally.
Examples
library(dplyr)
library(forcats)
adrs <- tern_ex_adrs
adrs_labels <- formatters::var_labels(adrs)
adrs_f <- adrs %>%
filter(PARAMCD == "BESRSPI") %>%
mutate(rsp = AVALC == "CR")
# Typical analysis of two continuous biomarkers `BMRKR1` and `AGE`,
# in logistic regression models with one covariate `RACE`. The subgroups
# are defined by the levels of `BMRKR2`.
df <- extract_rsp_biomarkers(
variables = list(
rsp = "rsp",
biomarkers = c("BMRKR1", "AGE"),
covariates = "SEX",
subgroups = "BMRKR2"
),
data = adrs_f
)
df
#> biomarker biomarker_label n_tot n_rsp prop or
#> 1 BMRKR1 Continuous Level Biomarker 1 200 164 0.8200000 0.9755036
#> 2 AGE Age 200 164 0.8200000 0.9952416
#> 3 BMRKR1 Continuous Level Biomarker 1 70 53 0.7571429 1.1524547
#> 4 AGE Age 70 53 0.7571429 0.9261012
#> 5 BMRKR1 Continuous Level Biomarker 1 68 58 0.8529412 0.8773122
#> 6 AGE Age 68 58 0.8529412 0.9867104
#> 7 BMRKR1 Continuous Level Biomarker 1 62 53 0.8548387 0.8792921
#> 8 AGE Age 62 53 0.8548387 1.0630262
#> lcl ucl conf_level pval pval_label subgroup var
#> 1 0.8804862 1.080775 0.95 0.6352602 p-value (Wald) All Patients ALL
#> 2 0.9462617 1.046757 0.95 0.8530389 p-value (Wald) All Patients ALL
#> 3 0.9462127 1.403650 0.95 0.1584187 p-value (Wald) LOW BMRKR2
#> 4 0.8487519 1.010500 0.95 0.0844837 p-value (Wald) LOW BMRKR2
#> 5 0.7277189 1.057657 0.95 0.1699778 p-value (Wald) MEDIUM BMRKR2
#> 6 0.8798911 1.106498 0.95 0.8189816 p-value (Wald) MEDIUM BMRKR2
#> 7 0.7189748 1.075357 0.95 0.2103709 p-value (Wald) HIGH BMRKR2
#> 8 0.9595973 1.177603 0.95 0.2418840 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
# Here we group the levels of `BMRKR2` manually, and we add a stratification
# variable `STRATA1`. We also here use a continuous variable `EOSDY`
# which is then binarized internally (response is defined as this variable
# being larger than 750).
df_grouped <- extract_rsp_biomarkers(
variables = list(
rsp = "EOSDY",
biomarkers = c("BMRKR1", "AGE"),
covariates = "SEX",
subgroups = "BMRKR2",
strata = "STRATA1"
),
data = adrs_f,
groups_lists = list(
BMRKR2 = list(
"low" = "LOW",
"low/medium" = c("LOW", "MEDIUM"),
"low/medium/high" = c("LOW", "MEDIUM", "HIGH")
)
),
control = control_logistic(
response_definition = "I(response > 750)"
)
)
df_grouped
#> biomarker biomarker_label n_tot n_rsp prop or lcl ucl conf_level
#> 1 BMRKR1 Continuous Level Biomarker 1 200 0 0 NA NA NA 0.95
#> 2 AGE Age 200 0 0 NA NA NA 0.95
#> 3 BMRKR1 Continuous Level Biomarker 1 70 0 0 NA NA NA 0.95
#> 4 AGE Age 70 0 0 NA NA NA 0.95
#> 5 BMRKR1 Continuous Level Biomarker 1 138 0 0 NA NA NA 0.95
#> 6 AGE Age 138 0 0 NA NA NA 0.95
#> 7 BMRKR1 Continuous Level Biomarker 1 200 0 0 NA NA NA 0.95
#> 8 AGE Age 200 0 0 NA NA NA 0.95
#> pval pval_label subgroup var var_label
#> 1 NA p-value (Wald) All Patients ALL All Patients
#> 2 NA p-value (Wald) All Patients ALL All Patients
#> 3 NA p-value (Wald) low BMRKR2 Continuous Level Biomarker 2
#> 4 NA p-value (Wald) low BMRKR2 Continuous Level Biomarker 2
#> 5 NA p-value (Wald) low/medium BMRKR2 Continuous Level Biomarker 2
#> 6 NA p-value (Wald) low/medium BMRKR2 Continuous Level Biomarker 2
#> 7 NA p-value (Wald) low/medium/high BMRKR2 Continuous Level Biomarker 2
#> 8 NA p-value (Wald) low/medium/high BMRKR2 Continuous Level Biomarker 2
#> row_type
#> 1 content
#> 2 content
#> 3 analysis
#> 4 analysis
#> 5 analysis
#> 6 analysis
#> 7 analysis
#> 8 analysis