Get standardized adverse event counts.
Arguments
- y
numeric vector, number of events in the trials.
- n
numeric vector, number of patients in the trials (either the total sample size or effective sample size, see Details for more information).
- time
numeric vector, strictly positive, time at risk in the trials.
- time_std
numeric, the unit of time we standardize to.
- as_data_frame
logical, TRUE to get results in a data-frame.
Details
To maintain consistency with other functions that create priors,
such as stan_map_summary_data, we use the variable names
y, n and time. It is recommended to use the total
number of events in the trial (one event per subject is counted) and
effective sample size (the number of subjects who completed the study plus
fractional dropouts) as the number of patients in the trial (n).
The planned study duration of the blinded ongoing study is used for
standardization (time_std).
The returned data-frame includes the standardized adverse event counts using the following formula:
\(y^* = n * (1 - (1 - (y/n))^{time_{std} / time})\)
Note also that when any is a vector, y, n, time_std,
and time are recycled using default R and tibble recycling
behaviors.
Examples
# In a trial of ten patients, one experienced the event, with follow-up twice
# the length of our reference period:
get_std_event_counts(y = 1, n = 10, time = 2, time_std = 1)
#> [1] 0.513167
# In a trial of ten patients, one experienced the event, with follow-up half
# the length of our reference period:
get_std_event_counts(y = 1, n = 10, time = 0.5, time_std = 1)
#> [1] 1.9
# Those two examples in one call, with data-frame output:
get_std_event_counts(y = 1, n = 10, time = c(2, 0.5), time_std = 1,
as_data_frame = TRUE)
#> # A tibble: 2 × 5
#> y n time time_std y_std
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 10 2 1 0.513
#> 2 1 10 0.5 1 1.9