Difference Test for Two Proportions

Usage

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")
)

test_proportion_diff(
  lyt,
  vars,
  ...,
  var_labels = vars,
  show_labels = "hidden",
  table_names = vars,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

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

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

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.

lyt

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

vars

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

...

other arguments are passed to s_test_proportion_diff().

var_labels

character for label.

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.

tbl

(matrix)
matrix with two groups in rows and the binary response (TRUE/FALSE) in columns.

Value

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.

Functions

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

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

• test_proportion_diff(): Layout creating function which can be used for creating tables, which can take statistics function arguments and additional format arguments.

See also

Examples

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

# Internal function - s_test_proportion_diff
if (FALSE) {
s_test_proportion_diff(
  df = subset(dta, grp == "A"),
  .var = "rsp",
  .ref_group = subset(dta, grp == "B"),
  .in_ref_col = FALSE,
  variables = list(strata = "strat"),
  method = "cmh"
)
}

# Internal function - a_test_proportion_diff
if (FALSE) {
a_test_proportion_diff(
  df = subset(dta, grp == "A"),
  .var = "rsp",
  .ref_group = subset(dta, grp == "B"),
  .in_ref_col = FALSE,
  variables = list(strata = "strat"),
  method = "cmh"
)
}

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

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