Proportion difference

Usage

estimate_proportion_diff(
  lyt,
  vars,
  variables = list(strata = NULL),
  conf_level = 0.95,
  method = c("waldcc", "wald", "cmh", "ha", "newcombe", "newcombecc", "strat_newcombe",
    "strat_newcombecc"),
  weights_method = "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_proportion_diff(
  df,
  .var,
  .ref_group,
  .in_ref_col,
  variables = list(strata = NULL),
  conf_level = 0.95,
  method = c("waldcc", "wald", "cmh", "ha", "newcombe", "newcombecc", "strat_newcombe",
    "strat_newcombecc"),
  weights_method = "cmh"
)

a_proportion_diff(
  df,
  .var,
  .ref_group,
  .in_ref_col,
  variables = list(strata = NULL),
  conf_level = 0.95,
  method = c("waldcc", "wald", "cmh", "ha", "newcombe", "newcombecc", "strat_newcombe",
    "strat_newcombecc"),
  weights_method = "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.
conf_level: (proportion)
confidence level of the interval.
method: (string)
the method used for the confidence interval estimation.
weights_method: (string)
weights method. Can be either "cmh" or "heuristic" and directs the way weights are estimated.
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("estimate_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

estimate_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_proportion_diff() to the table layout.

s_proportion_diff() returns a named list of elements diff and diff_ci.

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

Functions

estimate_proportion_diff(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().
s_proportion_diff(): Statistics function estimating the difference in terms of responder proportion.
a_proportion_diff(): Formatted analysis function which is used as afun in estimate_proportion_diff().

Note

When performing an unstratified analysis, methods "cmh", "strat_newcombe", and "strat_newcombecc" are not permitted.

Examples

## "Mid" case: 4/4 respond in group A, 1/2 respond in group B.
nex <- 100 # Number of example rows
dta <- data.frame(
  "rsp" = sample(c(TRUE, FALSE), nex, TRUE),
  "grp" = sample(c("A", "B"), nex, TRUE),
  "f1" = sample(c("a1", "a2"), nex, TRUE),
  "f2" = sample(c("x", "y", "z"), nex, TRUE),
  stringsAsFactors = TRUE
)

l <- basic_table() %>%
  split_cols_by(var = "grp", ref_group = "B") %>%
  estimate_proportion_diff(
    vars = "rsp",
    conf_level = 0.90,
    method = "ha"
  )

build_table(l, df = dta)
#>                                        A         B
#> ——————————————————————————————————————————————————
#> Difference in Response rate (%)       12.0        
#>   90% CI (Anderson-Hauck)         (-5.4, 29.4)    

s_proportion_diff(
  df = subset(dta, grp == "A"),
  .var = "rsp",
  .ref_group = subset(dta, grp == "B"),
  .in_ref_col = FALSE,
  conf_level = 0.90,
  method = "ha"
)
#> $diff
#> [1] 12
#> attr(,"label")
#> [1] "Difference in Response rate (%)"
#> 
#> $diff_ci
#> [1] -5.374519 29.374519
#> attr(,"label")
#> [1] "90% CI (Anderson-Hauck)"
#> 

# CMH example with strata
s_proportion_diff(
  df = subset(dta, grp == "A"),
  .var = "rsp",
  .ref_group = subset(dta, grp == "B"),
  .in_ref_col = FALSE,
  variables = list(strata = c("f1", "f2")),
  conf_level = 0.90,
  method = "cmh"
)
#> $diff
#> [1] 12.05847
#> attr(,"label")
#> [1] "Difference in Response rate (%)"
#> 
#> $diff_ci
#> [1] -2.67057 26.78750
#> attr(,"label")
#> [1] "90% CI (CMH, without correction)"
#> 

a_proportion_diff(
  df = subset(dta, grp == "A"),
  .var = "rsp",
  .ref_group = subset(dta, grp == "B"),
  .in_ref_col = FALSE,
  conf_level = 0.90,
  method = "ha"
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>   row_name formatted_cell indent_mod                       row_label
#> 1     diff           12.0          0 Difference in Response rate (%)
#> 2  diff_ci   (-5.4, 29.4)          1         90% CI (Anderson-Hauck)

Usage

Arguments

Value

Functions

Note

See also

Examples