Incidence rate estimation

The analyze function estimate_incidence_rate() creates a layout element to estimate an event rate adjusted for person-years at risk, otherwise known as incidence rate. The primary analysis variable specified via vars is the person-years at risk. In addition to this variable, the n_events variable for number of events observed (where a value of 1 means an event was observed and 0 means that no event was observed) must also be specified.

estimate_incidence_rate(
  lyt,
  vars,
  n_events,
  id_var = "USUBJID",
  control = control_incidence_rate(),
  na_str = default_na_str(),
  nested = TRUE,
  summarize = FALSE,
  label_fmt = "%s - %.labels",
  ...,
  show_labels = "hidden",
  table_names = vars,
  .stats = c("person_years", "n_events", "rate", "rate_ci"),
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

s_incidence_rate(
  df,
  .var,
  n_events,
  is_event = lifecycle::deprecated(),
  id_var = "USUBJID",
  control = control_incidence_rate()
)

a_incidence_rate(
  df,
  labelstr = "",
  .var,
  .df_row,
  n_events,
  id_var = "USUBJID",
  control = control_incidence_rate(),
  .stats = NULL,
  .formats = c(person_years = "xx.x", n_events = "xx", rate = "xx.xx", rate_ci =
    "(xx.xx, xx.xx)", n_unique = "xx", n_rate = "xx (xx.x)"),
  .labels = NULL,
  .indent_mods = NULL,
  na_str = default_na_str(),
  label_fmt = "%s - %.labels"
)

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

(string)
name of integer variable indicating whether an event has been observed (1) or not (0).

id_var

(string)
name of variable used as patient identifier if "n_unique" is included in .stats. Defaults to "USUBJID".

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.

summarize

(flag)
whether the function should act as an analyze function (summarize = FALSE), or a summarize function (summarize = TRUE). Defaults to FALSE.

label_fmt

(string)
how labels should be formatted after a row split occurs if summarize = TRUE. The string should use "%s" to represent row split levels, and "%.labels" to represent labels supplied to the .labels argument. Defaults to "%s - %.labels".

...

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: 'person_years', 'n_events', 'rate', 'rate_ci', 'n_unique', 'n_rate'

.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.

labelstr

(string)
label of the level of the parent split currently being summarized (must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information.

.df_row

(data.frame)
data frame across all of the columns for the given row split.

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.
- n_unique: Total number of patients with at least one event observed.
- n_rate: Total number of events observed & estimated incidence rate.

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

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

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")),
  STRATA1 = factor(c("X", "Y", "Y", "X", "X", "Y"))
)
df$n_events <- 1 - df$CNSR

basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM") %>%
  estimate_incidence_rate(
    vars = "AVAL",
    n_events = "n_events",
    control = control_incidence_rate(
      input_time_unit = "month",
      num_pt_year = 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)

# summarize = TRUE
basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM") %>%
  split_rows_by("STRATA1", child_labels = "visible") %>%
  estimate_incidence_rate(
    vars = "AVAL",
    n_events = "n_events",
    .stats = c("n_unique", "n_rate"),
    summarize = TRUE,
    label_fmt = "%.labels"
  ) %>%
  build_table(df)
#>                                                                          A         B   
#>                                                                        (N=3)     (N=3) 
#> ———————————————————————————————————————————————————————————————————————————————————————
#> X                                                                                      
#>   Total number of patients with at least one adverse event               1         2   
#>   Number of adverse events observed (AE rate per 100 patient-years)   1 (9.9)   2 (5.1)
#> Y                                                                                      
#>   Total number of patients with at least one adverse event               0         1   
#>   Number of adverse events observed (AE rate per 100 patient-years)   0 (0.0)   1 (4.3)

a_incidence_rate(
  df,
  .var = "AVAL",
  .df_row = df,
  n_events = "n_events"
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>       row_name formatted_cell indent_mod
#> 1 person_years          108.7          0
#> 2     n_events              4          0
#> 3         rate           3.68          0
#> 4      rate_ci   (0.07, 7.29)          0
#> 5     n_unique              4          0
#> 6       n_rate        4 (3.7)          0
#>                                                           row_label
#> 1                                       Total patient-years at risk
#> 2                                 Number of adverse events observed
#> 3                                     AE rate per 100 patient-years
#> 4                                                            95% CI
#> 5          Total number of patients with at least one adverse event
#> 6 Number of adverse events observed (AE rate per 100 patient-years)

Arguments

Value

Functions

See also

Examples