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())
h_tab_rsp_one_biomarker(df, vars, na_str = default_na_str(), .indent_mods = 0L)
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_tab_rsp_one_biomarker()
returns an rtables
table object with the given statistics arranged in columns.
(named list
of string
)
list of additional analysis variables.
(string
)
the name of the biomarker variable.
(data.frame
)
the dataset containing the variables to summarize.
(named list
)
controls for the response definition and the
confidence level produced by control_logistic()
.
(data.frame
)
results for a single biomarker, as part of what is
returned by extract_rsp_biomarkers()
(it needs a couple of columns which are
added by that high-level function relative to what is returned by h_logistic_mult_cont_df()
,
see the example).
(character
)
the names of statistics to be reported among:
n_tot
: Total number of patients per group.
n_rsp
: Total number of responses per group.
prop
: Total response proportion per group.
or
: Odds ratio.
ci
: Confidence interval of odds ratio.
pval
: p-value of the effect.
Note, the statistics n_tot
, or
and ci
are required.
(string
)
string used to replace all NA
or empty values in the output.
(named integer
)
indent modifiers for the labels. Defaults to 0, which corresponds to the
unmodified default behavior. Can be negative.
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
.
h_tab_rsp_one_biomarker()
: Prepares a single sub-table given a df_sub
containing
the results for a single biomarker.
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, ]
)
# 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_rsp_one_biomarker(
df1,
vars = c("n_tot", "n_rsp", "prop", "or", "ci", "pval")
)
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