Estimation of Proportions

Usage

s_proportion(
  df,
  .var,
  conf_level = 0.95,
  method = c("waldcc", "wald", "clopper-pearson", "wilson", "wilsonc", "strat_wilson",
    "strat_wilsonc", "agresti-coull", "jeffreys"),
  weights = NULL,
  max_iterations = 50,
  variables = list(strata = NULL),
  long = FALSE
)

a_proportion(
  df,
  .var,
  conf_level = 0.95,
  method = c("waldcc", "wald", "clopper-pearson", "wilson", "wilsonc", "strat_wilson",
    "strat_wilsonc", "agresti-coull", "jeffreys"),
  weights = NULL,
  max_iterations = 50,
  variables = list(strata = NULL),
  long = FALSE
)

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

Arguments

df

(logical or data.frame)
if only a logical vector is used, it indicates whether each subject is a responder or not. TRUE represents a successful outcome. If a data.frame is provided, also the strata variable names must be provided in variables as a list element with the strata strings. In the case of data.frame, the logical vector of responses must be indicated as a variable name in .var.

.var

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

conf_level

(proportion)
confidence level of the interval.

method

(string)
the method used to construct the confidence interval for proportion of successful outcomes; one of waldcc, wald, clopper-pearson, wilson, wilsonc, strat_wilson, strat_wilsonc, agresti-coull or jeffreys.

weights

(numeric or NULL)
weights for each level of the strata. If NULL, they are estimated using the iterative algorithm proposed in Yan and Su (2010) that minimizes the weighted squared length of the confidence interval.

max_iterations

(count)
maximum number of iterations for the iterative procedure used to find estimates of optimal weights.

variables

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

long

(flag)
a long description is required.

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 ultimately conveyed to s_proportion().

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.

Functions

• s_proportion(): statistics function estimating a proportion along with its confidence interval.

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

• estimate_proportion(): used in a rtables pipeline.

See also

Examples

# Case with only logical vector.
rsp_v <- c(1, 0, 1, 0, 1, 1, 0, 0)
s_proportion(rsp_v)
#> $n_prop
#> [1] 4.0 0.5
#> attr(,"label")
#> [1] "Responders"
#> 
#> $prop_ci
#> [1]  9.102404 90.897596
#> attr(,"label")
#> [1] "95% CI (Wald, with correction)"
#> 

# Example for Stratified Wilson CI
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
)

s_proportion(
  df = dta,
  .var = "rsp",
  variables = list(strata = c("f1", "f2")),
  conf_level = 0.90,
  method = "strat_wilson"
)
#> $n_prop
#> [1] 49.00  0.49
#> attr(,"label")
#> [1] "Responders"
#> 
#> $prop_ci
#>    lower    upper 
#> 40.80675 56.65017 
#> attr(,"label")
#> [1] "90% CI (Stratified Wilson, without correction)"
#> 

dta_test <- data.frame(
  USUBJID = paste0("S", 1:12),
  ARM     = 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))
)

basic_table() %>%
  split_cols_by("ARM") %>%
  estimate_proportion(vars = "AVAL") %>%
  build_table(df = dta_test)
#>                                        A              B              C     
#> ———————————————————————————————————————————————————————————————————————————
#> Responders                        4 (100.0%)      2 (50.0%)      0 (0.0%)  
#> 95% CI (Wald, with correction)   (87.5, 100.0)   (0.0, 100.0)   (0.0, 12.5)