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Estimation of Proportions per Level of Factor

Source: R/estimate_multinomial_rsp.R
estimate_multinomial_rsp.Rd

[Stable]

Estimate the proportion along with confidence interval of a proportion regarding the level of a factor.

Usage 

s_length_proportion(x, .N_col, ...)

a_length_proportion(x, .N_col, ...)

estimate_multinomial_response(
  lyt,
  var,
  ...,
  show_labels = "hidden",
  table_names = var,
  .stats = "prop_ci",
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

x

(numeric)
vector of numbers we want to analyze.

.N_col

(count)
row-wise N (row group count) for the group of observations being analyzed (i.e. with no column-based subsetting) that is passed by rtables.

...

additional arguments for the lower level functions.

lyt

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

var

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

show_labels

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

table_names

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

.stats

(character)
statistics to select for the table.

.formats

(named character or list)
formats for the statistics.

.labels

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

.indent_mods

(named integer)
indent modifiers for the labels.

Value

See s_proportion() for statistics and additional possible arguments.

Functions

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

  • a_length_proportion(): Formatted Analysis function which can be further customized by calling rtables::make_afun() on it. It is used as afun in rtables::analyze().

  • estimate_multinomial_response(): Analyze Function which adds the multinomial proportion analysis to the input layout. Note that additional formatting arguments can be used here.

See also

Relevant description function d_onco_rsp_label().

Examples 

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                      row_label
#> 1   n_prop     10 (10.0%)          0                     Responders
#> 2  prop_ci  (3.62, 16.38)          0 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                      row_label
#> 1   n_prop       0 (0.0%)          0                     Responders
#> 2  prop_ci   (0.00, 0.50)          0 95% CI (Wald, with correction)
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)

html <- as_html(tbl)
html
#> <div class="rtables-all-parts-block rtables-container">
#>   <table class="table table-condensed table-hover">
#>     <tr>
#>       <th style="white-space:pre;"></th>
#>       <th class="text-center">A</th>
#>       <th class="text-center">B</th>
#>       <th class="text-center">C</th>
#>     </tr>
#>     <tr>
#>       <td class="text-left">Complete Response (CR)</td>
#>       <td class="text-center">0 (0.0%)</td>
#>       <td class="text-center">2 (50.0%)</td>
#>       <td class="text-center">4 (100.0%)</td>
#>     </tr>
#>     <tr>
#>       <td class="text-left" style="padding-left: 3ch">95% CI (Wald, with correction)</td>
#>       <td class="text-center">(0.00, 12.50)</td>
#>       <td class="text-center">(0.00, 100.00)</td>
#>       <td class="text-center">(87.50, 100.00)</td>
#>     </tr>
#>     <tr>
#>       <td class="text-left">Partial Response (PR)</td>
#>       <td class="text-center">4 (100.0%)</td>
#>       <td class="text-center">2 (50.0%)</td>
#>       <td class="text-center">0 (0.0%)</td>
#>     </tr>
#>     <tr>
#>       <td class="text-left" style="padding-left: 3ch">95% CI (Wald, with correction)</td>
#>       <td class="text-center">(87.50, 100.00)</td>
#>       <td class="text-center">(0.00, 100.00)</td>
#>       <td class="text-center">(0.00, 12.50)</td>
#>     </tr>
#>     <caption style="caption-side:top;"><div class="rtables-titles-block rtables-container">
#>         <div class="rtables-main-titles-block rtables-container">
#>           <p class="rtables-main-title"></p>
#>         </div>
#>         <div class="rtables-subtitles-block rtables-container"></div>
#>       </div>
#>     </caption>
#>   </table>
#>   <div class="rtables-footers-block rtables-container"></div>
#> </div>
if (FALSE) {
Viewer(html)
}