Estimation of Proportions — estimate

Estimate the proportion of responders within a studied population.

Usage

prop_wilson(rsp, conf_level, correct = FALSE)

prop_clopper_pearson(rsp, conf_level)

prop_wald(rsp, conf_level, correct = FALSE)

prop_agresti_coull(rsp, conf_level)

prop_jeffreys(rsp, conf_level)

s_proportion(
  x,
  conf_level = 0.95,
  method = c("waldcc", "wald", "clopper-pearson", "wilson", "wilsonc", "agresti-coull",
    "jeffreys"),
  long = FALSE
)

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

a_proportion(
  x,
  conf_level = 0.95,
  method = c("waldcc", "wald", "clopper-pearson", "wilson", "wilsonc", "agresti-coull",
    "jeffreys"),
  long = FALSE
)

Arguments

rsp: (logical)
whether each subject is a responder or not.
conf_level: (proportion)
confidence level of the interval.
correct: (flag)
apply continuity correction.
x: (logical)
whether each subject is a responder or not. TRUE represents a successful outcome.
method: (string)
the method used to construct the confidence interval for proportion of successful outcomes; one of waldcc, wald, clopper-pearson, wilson, agresti-coull or jeffreys.
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

prop_wilson(): the Wilson interval calls stats::prop.test(). Also referred to as Wilson score interval.
prop_clopper_pearson(): the Clopper-Pearson interval calls stats::binom.test(). Also referred to as the exact method.
prop_wald(): the Wald interval follows the usual textbook definition for a single proportion confidence interval using the normal approximation.
prop_agresti_coull(): the Agresti-Coull interval was created by Alan Agresti and Brent Coull and can be understood (for 95% CI) as adding two successes and two failures to the data, and then using the Wald formula to construct a CI.
prop_jeffreys(): the Jeffreys interval is an equal-tailed interval based on the non-informative Jeffreys prior for a binomial proportion.
s_proportion(): statistics function estimating a proportion along with its confidence interval.
estimate_proportion(): used in a rtables pipeline.
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().

Examples

rsp <- c(
  TRUE, TRUE, TRUE, TRUE, TRUE,
  FALSE, FALSE, FALSE, FALSE, FALSE
)
prop_wilson(rsp, conf_level = 0.9)
#> [1] 0.2692718 0.7307282

prop_clopper_pearson(rsp, conf_level = .95)
#> [1] 0.187086 0.812914

prop_wald(rsp, conf_level = 0.95)
#> [1] 0.1901025 0.8098975
prop_wald(rsp, conf_level = 0.95, correct = TRUE)
#> [1] 0.1401025 0.8598975

prop_agresti_coull(rsp, conf_level = 0.95)
#> [1] 0.2365931 0.7634069

prop_jeffreys(rsp, conf_level = 0.95)
#> [1] 0.2235287 0.7764713
s_proportion(c(1, 0, 1, 0))
#> $n_prop
#> [1] 2.0 0.5
#> attr(,"label")
#> [1] "Responders"
#> 
#> $prop_ci
#> [1]   0 100
#> attr(,"label")
#> [1] "95% CI (Wald, with 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)
a_proportion(c(1, 0, 1, 0))
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>   row_name formatted_cell indent_mod                      row_label
#> 1   n_prop      2 (50.0%)          0                     Responders
#> 2  prop_ci   (0.0, 100.0)          0 95% CI (Wald, with correction)