Difference test for two proportions

Various tests were implemented to test the difference between two proportions.

Usage

test_proportion_diff(
  lyt,
  vars,
  variables = list(strata = NULL),
  method = c("chisq", "schouten", "fisher", "cmh"),
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  var_labels = vars,
  show_labels = "hidden",
  table_names = vars,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

s_test_proportion_diff(
  df,
  .var,
  .ref_group,
  .in_ref_col,
  variables = list(strata = NULL),
  method = c("chisq", "schouten", "fisher", "cmh")
)

a_test_proportion_diff(
  df,
  .var,
  .ref_group,
  .in_ref_col,
  variables = list(strata = NULL),
  method = c("chisq", "schouten", "fisher", "cmh")
)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

vars

(character)
variable names for the primary analysis variable to be iterated over.

variables

(named list of string)
list of additional analysis variables.

method

(string)
one of chisq, cmh, fisher, or schouten; specifies the test used to calculate the p-value.

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.

var_labels

(character)
variable labels.

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. Run get_stats("test_proportion_diff") to see available statistics for this function.

.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.

df

(data.frame)
data set containing all analysis variables.

.var

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

.ref_group

(data.frame or vector)
the data corresponding to the reference group.

.in_ref_col

(flag)
TRUE when working with the reference level, FALSE otherwise.

Value

• test_proportion_diff() 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_test_proportion_diff() to the table layout.

• s_test_proportion_diff() returns a named list with a single item pval with an attribute label describing the method used. The p-value tests the null hypothesis that proportions in two groups are the same.

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

Functions

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

• s_test_proportion_diff(): Statistics function which tests the difference between two proportions.

• a_test_proportion_diff(): Formatted analysis function which is used as afun in test_proportion_diff().

See also

h_prop_diff_test

Examples

dta <- data.frame(
  rsp = sample(c(TRUE, FALSE), 100, TRUE),
  grp = factor(rep(c("A", "B"), each = 50)),
  strata = factor(rep(c("V", "W", "X", "Y", "Z"), each = 20))
)

# With `rtables` pipelines.
l <- basic_table() %>%
  split_cols_by(var = "grp", ref_group = "B") %>%
  test_proportion_diff(
    vars = "rsp",
    method = "cmh", variables = list(strata = "strata")
  )

build_table(l, df = dta)
#>                                              A      B
#> —————————————————————————————————————————————————————
#>   p-value (Cochran-Mantel-Haenszel Test)   1.0000