h_response_biomarkers_subgroups | R Documentation |
Helper functions which are documented here separately to not confuse the user when reading about the user-facing functions.
h_rsp_to_logistic_variables(variables, biomarker)
h_logistic_mult_cont_df(variables, data, control = control_logistic())
variables |
(named |
biomarker |
( |
data |
( |
control |
(named |
h_rsp_to_logistic_variables()
returns a named list
of elements response
, arm
, covariates
, and strata
.
h_logistic_mult_cont_df()
returns a data.frame
containing estimates and statistics for the selected biomarkers.
h_rsp_to_logistic_variables()
: helps with converting the "response" function variable list
to the "logistic regression" variable list. The reason is that currently there is an
inconsistency between the variable names accepted by extract_rsp_subgroups()
and fit_logistic()
.
h_logistic_mult_cont_df()
: 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 in a given single data set.
variables
corresponds to 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
and strata
.
library(dplyr)
library(forcats)
adrs <- tern_ex_adrs
adrs_labels <- formatters::var_labels(adrs)
adrs_f <- adrs %>%
filter(PARAMCD == "BESRSPI") %>%
mutate(rsp = AVALC == "CR")
formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")
# This is how the variable list is converted internally.
h_rsp_to_logistic_variables(
variables = list(
rsp = "RSP",
covariates = c("A", "B"),
strata = "D"
),
biomarker = "AGE"
)
# For a single population, estimate separately the effects
# of two biomarkers.
df <- h_logistic_mult_cont_df(
variables = list(
rsp = "rsp",
biomarkers = c("BMRKR1", "AGE"),
covariates = "SEX"
),
data = adrs_f
)
df
# If the data set is empty, still the corresponding rows with missings are returned.
h_coxreg_mult_cont_df(
variables = list(
rsp = "rsp",
biomarkers = c("BMRKR1", "AGE"),
covariates = "SEX",
strata = "STRATA1"
),
data = adrs_f[NULL, ]
)
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