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Tabulate binary response by subgroup

Source: R/response_subgroups.R
response_subgroups.Rd

[Stable]

The tabulate_rsp_subgroups() function creates a layout element to tabulate binary response by subgroup, returning statistics including response rate and odds ratio for each population subgroup. The table is created from df, a list of data frames returned by extract_rsp_subgroups(), with the statistics to include specified via the vars parameter.

A forest plot can be created from the resulting table using the g_forest() function.

Usage 

tabulate_rsp_subgroups(
  lyt,
  df,
  vars = c("n_tot", "n", "prop", "or", "ci"),
  groups_lists = list(),
  label_all = lifecycle::deprecated(),
  riskdiff = NULL,
  na_str = default_na_str(),
  ...,
  .stat_names = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

a_response_subgroups(
  df,
  labelstr = "",
  ...,
  .stats = NULL,
  .stat_names = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

df

(list)
a list of data frames containing all analysis variables. List should be created using extract_rsp_subgroups().

vars

(character)
the names of statistics to be reported among:

  • n: Total number of observations per group.

  • n_rsp: Number of responders per group.

  • prop: Proportion of responders.

  • n_tot: Total number of observations.

  • 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.

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.

label_all

(string)
label for the total population analysis.

riskdiff

(list)
if a risk (proportion) difference column should be added, a list of settings to apply within the column. See control_riskdiff() for details. If NULL, no risk difference column will be added. If riskdiff$arm_x and riskdiff$arm_y are NULL, the first level of df$prop$arm will be used as arm_x and the second level as arm_y.

na_str

(string)
string used to replace all NA or empty values in the output.

...

additional arguments for the lower level functions.

.stat_names

(character)
names of the statistics that are passed directly to name single statistics (.stats). This option is visible when producing rtables::as_result_df() with make_ard = TRUE.

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for more information on the "auto" setting.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

labelstr

(string)
label of the level of the parent split currently being summarized (must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information.

.stats

(character)
statistics to select for the table.

Value

An rtables table summarizing binary response by subgroup.

Details

These functions create a layout starting from a data frame which contains the required statistics. Tables typically used as part of forest plot.

Functions

  • tabulate_rsp_subgroups(): Table-creating function which creates a table summarizing binary response by subgroup. This function is a wrapper for rtables::analyze_colvars() and rtables::summarize_row_groups().

  • a_response_subgroups(): Formatted analysis function which is used as afun in tabulate_rsp_subgroups().

See also

extract_rsp_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"), strata = "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
#> 

# Table with default columns
basic_table() %>%
  tabulate_rsp_subgroups(df)
#>                                             B: Placebo           A: Drug X                                 
#> Baseline Risk Factors          Total n   n    Response (%)   n    Response (%)   Odds Ratio      95% CI    
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————
#> All Patients                     142     73      68.5%       69      85.5%          2.71      (1.18, 6.24) 
#> Sex                                                                                                        
#>   F                              78      40      62.5%       38      94.7%         10.80      (2.27, 51.45)
#>   M                              64      33      75.8%       31      74.2%          0.92      (0.30, 2.85) 
#> Continuous Level Biomarker 2                                                                               
#>   LOW                            50      24      54.2%       26      80.8%          3.55      (1.00, 12.57)
#>   MEDIUM                         49      23      73.9%       26      88.5%          2.71      (0.59, 12.39)
#>   HIGH                           43      26      76.9%       17      88.2%          2.25      (0.40, 12.75)

# Table with selected columns
basic_table() %>%
  tabulate_rsp_subgroups(
    df = df,
    vars = c("n_tot", "n", "n_rsp", "prop", "or", "ci")
  )
#>                                                    B: Placebo                       A: Drug X                                        
#> Baseline Risk Factors          Total n   n    Responders   Response (%)   n    Responders   Response (%)   Odds Ratio      95% CI    
#> —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> All Patients                     142     73       50          68.5%       69       59          85.5%          2.71      (1.18, 6.24) 
#> Sex                                                                                                                                  
#>   F                              78      40       25          62.5%       38       36          94.7%         10.80      (2.27, 51.45)
#>   M                              64      33       25          75.8%       31       23          74.2%          0.92      (0.30, 2.85) 
#> Continuous Level Biomarker 2                                                                                                         
#>   LOW                            50      24       13          54.2%       26       21          80.8%          3.55      (1.00, 12.57)
#>   MEDIUM                         49      23       17          73.9%       26       23          88.5%          2.71      (0.59, 12.39)
#>   HIGH                           43      26       20          76.9%       17       15          88.2%          2.25      (0.40, 12.75)

# Table with risk difference column added
basic_table() %>%
  tabulate_rsp_subgroups(
    df,
    riskdiff = control_riskdiff(
      arm_x = levels(df$prop$arm)[1],
      arm_y = levels(df$prop$arm)[2]
    )
  )
#>                                             B: Placebo           A: Drug X                                                                
#> Baseline Risk Factors          Total n   n    Response (%)   n    Response (%)   Odds Ratio      95% CI       Risk Difference (%) (95% CI)
#> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> All Patients                     142     73      68.5%       69      85.5%          2.71      (1.18, 6.24)        -17.0 (-30.5 - -3.5)    
#> Sex                                                                                                                                       
#>   F                              78      40      62.5%       38      94.7%         10.80      (2.27, 51.45)      -32.2 (-48.8 - -15.6)    
#>   M                              64      33      75.8%       31      74.2%          0.92      (0.30, 2.85)         1.6 (-19.7 - 22.8)     
#> Continuous Level Biomarker 2                                                                                                              
#>   LOW                            50      24      54.2%       26      80.8%          3.55      (1.00, 12.57)       -26.6 (-51.6 - -1.6)    
#>   MEDIUM                         49      23      73.9%       26      88.5%          2.71      (0.59, 12.39)       -14.5 (-36.3 - 7.2)     
#>   HIGH                           43      26      76.9%       17      88.2%          2.25      (0.40, 12.75)       -11.3 (-33.6 - 11.0)