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Tabulate Binary Response by Subgroup

Source: R/response_subgroups.R
response_subgroups.Rd

[Stable]

Tabulate statistics such as response rate and odds ratio for population subgroups.

Usage 

a_response_subgroups(
  .formats = list(n = "xx", n_rsp = "xx", prop = "xx.x%", n_tot = "xx", or =
    list(format_extreme_values(2L)), ci = list(format_extreme_values_ci(2L)), pval =
    "x.xxxx | (<0.0001)")
)

tabulate_rsp_subgroups(lyt, df, vars = c("n_tot", "n", "prop", "or", "ci"))

Arguments

.formats

(named character or list)
formats for the statistics.

lyt

(layout)
input layout where analyses will be added to.

df

(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.

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.

label_all

(string)
label for the total population analysis.

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.

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

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

  • 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().

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
#> 

## Table with default columns.
basic_table() %>%
  tabulate_rsp_subgroups(df)
#> Baseline Risk Factors                       B: Placebo           A: Drug X                                 
#>                                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")
  )
#> Baseline Risk Factors                              B: Placebo                       A: Drug X                                        
#>                                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)