incidence_rate: Incidence rate

Description

Estimate the event rate adjusted for person-years at risk, otherwise known as incidence rate. Primary analysis variable is the person-years at risk.

Usage

estimate_incidence_rate(
  lyt,
  vars,
  n_events,
  control = control_incidence_rate(),
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  show_labels = "hidden",
  table_names = vars,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)
s_incidence_rate(
  df,
  .var,
  n_events,
  is_event,
  control = control_incidence_rate()
)
a_incidence_rate(
  df,
  .var,
  n_events,
  is_event,
  control = control_incidence_rate()
)

Value

estimate_incidence_rate() 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_incidence_rate() to the table layout.

s_incidence_rate() returns the following statistics:
- person_years: Total person-years at risk.
- n_events: Total number of events observed.
- rate: Estimated incidence rate.
- rate_ci: Confidence interval for the incidence rate.

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

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

vars

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

n_events

(integer(1))
number of events observed.

control

(list)
parameters for estimation details, specified by using the helper function control_incidence_rate(). Possible parameter options are:

conf_level (proportion)
confidence level for the estimated incidence rate.
conf_type (string)
normal (default), normal_log, exact, or byar for confidence interval type.
input_time_unit (string)
day, week, month, or year (default) indicating time unit for data input.
num_pt_year (numeric)
time unit for desired output (in person-years).

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. Run get_stats("estimate_incidence_rate") 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.

is_event

(flag)
TRUE if event, FALSE if time to event is censored.

Functions

estimate_incidence_rate(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().
s_incidence_rate(): Statistics function which estimates the incidence rate and the associated confidence interval.
a_incidence_rate(): Formatted analysis function which is used as afun in estimate_incidence_rate().

Examples

Run this code

library(dplyr)

df <- data.frame(
  USUBJID = as.character(seq(6)),
  CNSR = c(0, 1, 1, 0, 0, 0),
  AVAL = c(10.1, 20.4, 15.3, 20.8, 18.7, 23.4),
  ARM = factor(c("A", "A", "A", "B", "B", "B"))
) %>%
  mutate(is_event = CNSR == 0) %>%
  mutate(n_events = as.integer(is_event))

basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  estimate_incidence_rate(
    vars = "AVAL",
    n_events = "n_events",
    control = control_incidence_rate(
      input_time_unit = "month",
      num_pt_year = 100
    )
  ) %>%
  build_table(df)