Skip to contents

Prepare response data estimates for multiple biomarkers in a single data frame

Source: R/response_biomarkers_subgroups.R
extract_rsp_biomarkers.Rd

[Stable]

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