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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,
  nested = TRUE,
  ...,
  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

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

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.

show_labels

(string)
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. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

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.

Functions

  • 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().

  • 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().

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>
# \donttest{
Viewer(html)
# }