Difference Test for Two Proportions

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

Usage

prop_chisq(tbl)

prop_schouten(tbl)

prop_fisher(tbl)

prop_cmh(ary)

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,
  ...,
  show_labels = "hidden",
  table_names = vars,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

tbl: (matrix)
with two groups in rows and the binary response (TRUE/FALSE) in columns.
ary: (array, 3 dimensions)
with two groups in rows, the binary response (TRUE/FALSE) in columns, the strata in the third dimension.
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, 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().
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

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

prop_chisq(): performs Chi-Squared test. Internally calls stats::prop.test().
prop_schouten(): performs the Chi-Squared test with Schouten correction.
prop_fisher(): performs the Fisher's exact test. Internally calls stats::fisher.test().
prop_cmh(): performs stratified Cochran-Mantel-Haenszel test. Internally calls stats::mantelhaen.test(). Note that strata with less than two observations are automatically discarded.
s_test_proportion_diff(): Statistics function which test 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.

Examples

# Non-stratified proportion difference test

## Data
A <- 20
B <- 20
set.seed(1)
rsp <- c(
  sample(c(TRUE, FALSE), size = A, prob = c(3 / 4, 1 / 4), replace = TRUE),
  sample(c(TRUE, FALSE), size = A, prob = c(1 / 2, 1 / 2), replace = TRUE)
)
grp <- c(rep("A", A), rep("B", B))
tbl <- table(grp, rsp)

## Chi-Squared test
# Internal function - prop_chisq
if (FALSE) {
prop_chisq(tbl)
}

## Chi-Squared test + Schouten correction.
# Internal function - prop_schouten
if (FALSE) {
prop_schouten(tbl)
}

## Fisher's exact test
# Internal function - prop_fisher
if (FALSE) {
prop_fisher(tbl)
}

# Stratified proportion difference test

## Data
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))
tbl <- table(grp, rsp, strata)

## Cochran-Mantel-Haenszel test
# Internal function - prop_cmh
if (FALSE) {
prop_cmh(tbl)
}


# 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)       0.3736

Usage

Arguments

Value

Functions

See also

Examples