Proportion estimation

The analyze function estimate_proportion() creates a layout element to estimate the proportion of responders within a studied population. The primary analysis variable, vars, indicates whether a response has occurred for each record. See the method parameter for options of methods to use when constructing the confidence interval of the proportion. Additionally, a stratification variable can be supplied via the strata element of the variables argument.

estimate_proportion(
  lyt,
  vars,
  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,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  show_labels = "hidden",
  table_names = vars,
  .stats = c("n_prop", "prop_ci"),
  .stat_names = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

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,
  denom = c("n", "N_col", "N_row"),
  ...
)

a_proportion(
  df,
  ...,
  .stats = NULL,
  .stat_names = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

vars

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

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)
whether a long description is required.

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.

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.

Options are: 'n_prop', 'prop_ci'

.stat_names

(character)
names of the statistics that are passed directly to name single statistics (.stats). This option is visible when producing rtables::as_result_df() with make_ard = TRUE.

.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

(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.

denom

(string)
choice of denominator for proportion. Options are:

n: number of values in this row and column intersection.
N_row: total number of values in this row across columns.
N_col: total number of values in this column across rows.

Value

estimate_proportion() 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() to the table layout.

s_proportion() returns statistics n_prop (n and proportion) and prop_ci (proportion CI) for a given variable.

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

Functions

estimate_proportion(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().
s_proportion(): Statistics function estimating a proportion along with its confidence interval.
a_proportion(): Formatted analysis function which is used as afun in estimate_proportion().

Examples

dta_test <- data.frame(
  USUBJID = paste0("S", 1:12),
  ARM = rep(LETTERS[1:3], each = 4),
  AVAL = rep(LETTERS[1:3], each = 4)
) %>%
  dplyr::mutate(is_rsp = AVAL == "A")

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

# 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] 47.00  0.47
#> attr(,"label")
#> [1] "Responders"
#> 
#> $prop_ci
#>    lower    upper 
#> 38.15468 53.64792 
#> attr(,"label")
#> [1] "90% CI (Stratified Wilson, without correction)"
#>

Arguments

Value

Functions

See also

Examples