Estimate proportions of each level of a variable

The analyze & summarize function estimate_multinomial_response() creates a layout element to estimate the proportion and proportion confidence interval for each level of a factor variable. The primary analysis variable, var, should be a factor variable, the values of which will be used as labels within the output table.

Usage

estimate_multinomial_response(
  lyt,
  var,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  show_labels = "hidden",
  table_names = var,
  .stats = "prop_ci",
  .stat_names = NULL,
  .formats = list(prop_ci = "(xx.xx, xx.xx)"),
  .labels = NULL,
  .indent_mods = NULL
)

s_length_proportion(x, ..., .N_col)

a_length_proportion(
  x,
  ...,
  .stats = NULL,
  .stat_names = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

var

(string)
single variable name that is passed by rtables when requested by a statistics function.

na_str

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

nested

(flag)
whether this layout instruction should be applied within the existing layout structure _if possible (TRUE, the default) or as a new top-level element (FALSE). Ignored if it would nest a split. underneath analyses, which is not allowed.

...

additional arguments for the lower level functions.

show_labels

(string)
label visibility: one of "default", "visible" and "hidden".

table_names

(character)
this can be customized in the case that the same vars are analyzed multiple times, to avoid warnings from rtables.

.stats

(character)
statistics to select for the table.

Options are: 'n_prop', 'prop_ci'

.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.

x

(numeric)
vector of numbers we want to analyze.

.N_col

(integer(1))
column-wise N (column count) for the full column being analyzed that is typically passed by rtables.

Value

• estimate_multinomial_response() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_length_proportion() to the table layout.

• s_length_proportion() returns statistics from s_proportion().

• a_length_proportion() returns the corresponding list with formatted rtables::CellValue().

Functions

• estimate_multinomial_response(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze() and rtables::summarize_row_groups().

• s_length_proportion(): Statistics function which feeds the length of x as number of successes, and .N_col as total number of successes and failures into s_proportion().

• a_length_proportion(): Formatted analysis function which is used as afun in estimate_multinomial_response().

See also

Relevant description function d_onco_rsp_label().

Examples

library(dplyr)

# Use of the layout creating function.
dta_test <- data.frame(
  USUBJID = paste0("S", 1:12),
  ARM     = factor(rep(LETTERS[1:3], each = 4)),
  AVAL    = c(A = c(1, 1, 1, 1), B = c(0, 0, 1, 1), C = c(0, 0, 0, 0))
) %>% mutate(
  AVALC = factor(AVAL,
    levels = c(0, 1),
    labels = c("Complete Response (CR)", "Partial Response (PR)")
  )
)

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  estimate_multinomial_response(var = "AVALC")

tbl <- build_table(lyt, dta_test)

tbl
#>                                           A                B                 C       
#> —————————————————————————————————————————————————————————————————————————————————————
#> Complete Response (CR)                0 (0.0%)         2 (50.0%)        4 (100.0%)   
#>   95% CI (Wald, with correction)    (0.00, 12.50)    (0.00, 100.00)   (87.50, 100.00)
#> Partial Response (PR)                4 (100.0%)        2 (50.0%)         0 (0.0%)    
#>   95% CI (Wald, with correction)   (87.50, 100.00)   (0.00, 100.00)    (0.00, 12.50) 

s_length_proportion(rep("CR", 10), .N_col = 100)
#> $n_prop
#> [1] 10.0  0.1
#> attr(,"label")
#> [1] "Responders"
#> 
#> $prop_ci
#> [1]  3.620108 16.379892
#> attr(,"label")
#> [1] "95% CI (Wald, with correction)"
#> 
s_length_proportion(factor(character(0)), .N_col = 100)
#> $n_prop
#> [1] 0 0
#> attr(,"label")
#> [1] "Responders"
#> 
#> $prop_ci
#> [1] 0.0 0.5
#> attr(,"label")
#> [1] "95% CI (Wald, with correction)"
#> 

a_length_proportion(rep("CR", 10), .N_col = 100)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>                         row_name formatted_cell indent_mod
#> 1                     Responders     10 (10.0%)          0
#> 2 95% CI (Wald, with correction)    (3.6, 16.4)          0
#>                        row_label
#> 1                     Responders
#> 2 95% CI (Wald, with correction)
a_length_proportion(factor(character(0)), .N_col = 100)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>                         row_name formatted_cell indent_mod
#> 1                     Responders       0 (0.0%)          0
#> 2 95% CI (Wald, with correction)     (0.0, 0.5)          0
#>                        row_label
#> 1                     Responders
#> 2 95% CI (Wald, with correction)