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
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()
)
estimate_incidence_rate(
lyt,
vars,
...,
show_labels = "hidden",
table_names = vars,
.stats = NULL,
.formats = NULL,
.labels = NULL,
.indent_mods = NULL
)
Arguments
- df
(
data.frame
)
data set containing all analysis variables.- .var
(
string
)
single variable name that is passed byrtables
when requested by a statistics function.- n_events
(
integer
)
number of events observed.- is_event
(
logical
)TRUE
if event,FALSE
if time to event is censored.- control
-
(
list
)
parameters for estimation details, specified by using the helper functioncontrol_incidence_rate()
. Possible parameter options are:conf_level
(proportion
)
confidence level for the estimated incidence rate.conf_type
(string
)normal
(default),normal_log
,exact
, orbyar
for confidence interval type.time_unit_input
(string
)day
,week
,month
, oryear
(default) indicating time unit for data input.time_unit_output
(numeric
)
time unit for desired output (in person-years).
- lyt
(
layout
)
input layout where analyses will be added to.- vars
(
character
)
variable names for the primary analysis variable to be iterated over.- ...
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 case that the samevars
are analyzed multiple times, to avoid warnings fromrtables
.- .stats
(
character
)
statistics to select for the table.- .formats
(named
character
orlist
)
formats for the statistics.- .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.- person_years
(
numeric
)
total person-years at risk.- alpha
(
numeric
)
two-sided alpha-level for confidence interval.
Value
-
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 formattedrtables::CellValue()
.
estimate_incidence_rate()
returns a layout object suitable for passing to further layouting functions, or tortables::build_table()
. Adding this function to anrtable
layout will add formatted rows containing the statistics froms_incidence_rate()
to the table layout.
Functions
s_incidence_rate()
: Statistics function which estimates the incidence rate and the associated confidence interval.a_incidence_rate()
: Formatted analysis function which is used asafun
inestimate_incidence_rate()
.estimate_incidence_rate()
: Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper forrtables::analyze()
.
See also
control_incidence_rate()
and helper functions h_incidence_rate.
Examples
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))
# Internal function - s_incidence_rate
if (FALSE) {
s_incidence_rate(
df,
.var = "AVAL",
n_events = "n_events",
control = control_incidence_rate(
time_unit_input = "month",
time_unit_output = 100
)
)
}
# Internal function - a_incidence_rate
if (FALSE) {
a_incidence_rate(
df,
.var = "AVAL",
n_events = "n_events",
control = control_incidence_rate(time_unit_input = "month", time_unit_output = 100)
)
}
basic_table() %>%
split_cols_by("ARM") %>%
add_colcounts() %>%
estimate_incidence_rate(
vars = "AVAL",
n_events = "n_events",
control = control_incidence_rate(
time_unit_input = "month",
time_unit_output = 100
)
) %>%
build_table(df)
#> A B
#> (N=3) (N=3)
#> —————————————————————————————————————————————————————————————————————
#> Total patient-years at risk 3.8 5.2
#> Number of adverse events observed 1 3
#> AE rate per 100 patient-years 26.20 57.23
#> 95% CI (-25.15, 77.55) (-7.53, 122.00)