Prepare response data estimates for multiple biomarkers in a single data frame — extract_rsp_biomarkers • tern

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.

Usage

extract_rsp_biomarkers(
  variables,
  data,
  groups_lists = list(),
  control = control_logistic(),
  label_all = "All Patients"
)

Arguments

variables: (named list of string)
list of additional analysis variables.
data: (data.frame)
the dataset containing the variables to summarize.
groups_lists: (named list of list)
optionally contains for each subgroups variable 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 by control_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