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Prepares Response Data for Population Subgroups in Data Frames

Source: R/response_subgroups.R
extract_rsp_subgroups.Rd

[Stable]

Prepares response rates and odds ratios for population subgroups in data frames. Simple wrapper for h_odds_ratio_subgroups_df() and h_proportion_subgroups_df(). Result is a list of two data.frames: prop and or. variables corresponds to the names of variables found in data, passed as a named list and requires elements rsp, arm and optionally subgroups and strat. groups_lists optionally specifies groupings for subgroups variables.

Usage 

extract_rsp_subgroups(
  variables,
  data,
  groups_lists = list(),
  conf_level = 0.95,
  method = NULL,
  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.

conf_level

(proportion)
confidence level of the interval.

method

(string)
specifies the test used to calculate the p-value for the difference between two proportions. For options, see s_test_proportion_diff(). Default is NULL so no test is performed.

label_all

(string)
label for the total population analysis.

Value

A named list of two elements:

  • prop: A data.frame containing columns arm, n, n_rsp, prop, subgroup, var, var_label, and row_type.

  • or: A data.frame containing columns arm, n_tot, or, lcl, ucl, conf_level, subgroup, var, var_label, and row_type.

See also

response_subgroups

Examples 

library(dplyr)
library(forcats)

adrs <- tern_ex_adrs
adrs_labels <- formatters::var_labels(adrs)

adrs_f <- adrs %>%
  filter(PARAMCD == "BESRSPI") %>%
  filter(ARM %in% c("A: Drug X", "B: Placebo")) %>%
  droplevels() %>%
  mutate(
    # Reorder levels of factor to make the placebo group the reference arm.
    ARM = fct_relevel(ARM, "B: Placebo"),
    rsp = AVALC == "CR"
  )
formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")

# Unstratified analysis.
df <- extract_rsp_subgroups(
  variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
  data = adrs_f
)
df
#> $prop
#>           arm  n n_rsp      prop     subgroup    var
#> 1  B: Placebo 73    50 0.6849315 All Patients    ALL
#> 2   A: Drug X 69    59 0.8550725 All Patients    ALL
#> 3  B: Placebo 40    25 0.6250000            F    SEX
#> 4   A: Drug X 38    36 0.9473684            F    SEX
#> 5  B: Placebo 33    25 0.7575758            M    SEX
#> 6   A: Drug X 31    23 0.7419355            M    SEX
#> 7  B: Placebo 24    13 0.5416667          LOW BMRKR2
#> 8   A: Drug X 26    21 0.8076923          LOW BMRKR2
#> 9  B: Placebo 23    17 0.7391304       MEDIUM BMRKR2
#> 10  A: Drug X 26    23 0.8846154       MEDIUM BMRKR2
#> 11 B: Placebo 26    20 0.7692308         HIGH BMRKR2
#> 12  A: Drug X 17    15 0.8823529         HIGH BMRKR2
#>                       var_label row_type
#> 1                  All Patients  content
#> 2                  All Patients  content
#> 3                           Sex analysis
#> 4                           Sex analysis
#> 5                           Sex analysis
#> 6                           Sex analysis
#> 7  Continuous Level Biomarker 2 analysis
#> 8  Continuous Level Biomarker 2 analysis
#> 9  Continuous Level Biomarker 2 analysis
#> 10 Continuous Level Biomarker 2 analysis
#> 11 Continuous Level Biomarker 2 analysis
#> 12 Continuous Level Biomarker 2 analysis
#> 
#> $or
#>   arm n_tot        or       lcl       ucl conf_level     subgroup    var
#> 1       142  2.714000 1.1804488  6.239827       0.95 All Patients    ALL
#> 2        78 10.800000 2.2669576 51.452218       0.95            F    SEX
#> 3        64  0.920000 0.2966470  2.853223       0.95            M    SEX
#> 4        50  3.553846 1.0047370 12.570277       0.95          LOW BMRKR2
#> 5        49  2.705882 0.5911718 12.385232       0.95       MEDIUM BMRKR2
#> 6        43  2.250000 0.3970298 12.750933       0.95         HIGH BMRKR2
#>                      var_label row_type
#> 1                 All Patients  content
#> 2                          Sex analysis
#> 3                          Sex analysis
#> 4 Continuous Level Biomarker 2 analysis
#> 5 Continuous Level Biomarker 2 analysis
#> 6 Continuous Level Biomarker 2 analysis
#> 

# Stratified analysis.
df_strat <- extract_rsp_subgroups(
  variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2"), strat = "STRATA1"),
  data = adrs_f
)
df_strat
#> $prop
#>           arm  n n_rsp      prop     subgroup    var
#> 1  B: Placebo 73    50 0.6849315 All Patients    ALL
#> 2   A: Drug X 69    59 0.8550725 All Patients    ALL
#> 3  B: Placebo 40    25 0.6250000            F    SEX
#> 4   A: Drug X 38    36 0.9473684            F    SEX
#> 5  B: Placebo 33    25 0.7575758            M    SEX
#> 6   A: Drug X 31    23 0.7419355            M    SEX
#> 7  B: Placebo 24    13 0.5416667          LOW BMRKR2
#> 8   A: Drug X 26    21 0.8076923          LOW BMRKR2
#> 9  B: Placebo 23    17 0.7391304       MEDIUM BMRKR2
#> 10  A: Drug X 26    23 0.8846154       MEDIUM BMRKR2
#> 11 B: Placebo 26    20 0.7692308         HIGH BMRKR2
#> 12  A: Drug X 17    15 0.8823529         HIGH BMRKR2
#>                       var_label row_type
#> 1                  All Patients  content
#> 2                  All Patients  content
#> 3                           Sex analysis
#> 4                           Sex analysis
#> 5                           Sex analysis
#> 6                           Sex analysis
#> 7  Continuous Level Biomarker 2 analysis
#> 8  Continuous Level Biomarker 2 analysis
#> 9  Continuous Level Biomarker 2 analysis
#> 10 Continuous Level Biomarker 2 analysis
#> 11 Continuous Level Biomarker 2 analysis
#> 12 Continuous Level Biomarker 2 analysis
#> 
#> $or
#>   arm n_tot        or       lcl       ucl conf_level     subgroup    var
#> 1       142 2.6343899 1.1537821  6.015009       0.95 All Patients    ALL
#> 2        78 9.5946605 2.0379337 45.171985       0.95            F    SEX
#> 3        64 0.8947158 0.2936803  2.725809       0.95            M    SEX
#> 4        50 3.5976656 1.0101319 12.813374       0.95          LOW BMRKR2
#> 5        49 2.6242168 0.5162572 13.339308       0.95       MEDIUM BMRKR2
#> 6        43 2.2816865 0.4116391 12.647228       0.95         HIGH BMRKR2
#>                      var_label row_type
#> 1                 All Patients  content
#> 2                          Sex analysis
#> 3                          Sex analysis
#> 4 Continuous Level Biomarker 2 analysis
#> 5 Continuous Level Biomarker 2 analysis
#> 6 Continuous Level Biomarker 2 analysis
#> 

# Grouping of the BMRKR2 levels.
df_grouped <- extract_rsp_subgroups(
  variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
  data = adrs_f,
  groups_lists = list(
    BMRKR2 = list(
      "low" = "LOW",
      "low/medium" = c("LOW", "MEDIUM"),
      "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
    )
  )
)
df_grouped
#> $prop
#>           arm  n n_rsp      prop        subgroup    var
#> 1  B: Placebo 73    50 0.6849315    All Patients    ALL
#> 2   A: Drug X 69    59 0.8550725    All Patients    ALL
#> 3  B: Placebo 40    25 0.6250000               F    SEX
#> 4   A: Drug X 38    36 0.9473684               F    SEX
#> 5  B: Placebo 33    25 0.7575758               M    SEX
#> 6   A: Drug X 31    23 0.7419355               M    SEX
#> 7  B: Placebo 24    13 0.5416667             low BMRKR2
#> 8   A: Drug X 26    21 0.8076923             low BMRKR2
#> 9  B: Placebo 47    30 0.6382979      low/medium BMRKR2
#> 10  A: Drug X 52    44 0.8461538      low/medium BMRKR2
#> 11 B: Placebo 73    50 0.6849315 low/medium/high BMRKR2
#> 12  A: Drug X 69    59 0.8550725 low/medium/high BMRKR2
#>                       var_label row_type
#> 1                  All Patients  content
#> 2                  All Patients  content
#> 3                           Sex analysis
#> 4                           Sex analysis
#> 5                           Sex analysis
#> 6                           Sex analysis
#> 7  Continuous Level Biomarker 2 analysis
#> 8  Continuous Level Biomarker 2 analysis
#> 9  Continuous Level Biomarker 2 analysis
#> 10 Continuous Level Biomarker 2 analysis
#> 11 Continuous Level Biomarker 2 analysis
#> 12 Continuous Level Biomarker 2 analysis
#> 
#> $or
#>   arm n_tot        or      lcl       ucl conf_level        subgroup    var
#> 1       142  2.714000 1.180449  6.239827       0.95    All Patients    ALL
#> 2        78 10.800000 2.266958 51.452218       0.95               F    SEX
#> 3        64  0.920000 0.296647  2.853223       0.95               M    SEX
#> 4        50  3.553846 1.004737 12.570277       0.95             low BMRKR2
#> 5        99  3.116667 1.193409  8.139385       0.95      low/medium BMRKR2
#> 6       142  2.714000 1.180449  6.239827       0.95 low/medium/high BMRKR2
#>                      var_label row_type
#> 1                 All Patients  content
#> 2                          Sex analysis
#> 3                          Sex analysis
#> 4 Continuous Level Biomarker 2 analysis
#> 5 Continuous Level Biomarker 2 analysis
#> 6 Continuous Level Biomarker 2 analysis
#>