This is a function for designing a study with a binary outcome whose
inferential target is the relative risk. rr_design()
will calculate
the minimum number sample size, relative risk, power, or Type I error when
the other arguments are specified. The approximation used relies on the delta
method for the log relative risk.
Usage
rr_n_per_arm(
pi_1 = NULL,
pi_0 = NULL,
rr = NULL,
power = 0.8,
alpha = 0.05,
test_sides = 2
)
rr_power(
pi_1 = NULL,
pi_0 = NULL,
rr = NULL,
n_per_arm,
alpha = 0.05,
test_sides = 2
)
rr_alpha(
pi_1 = NULL,
pi_0 = NULL,
rr = NULL,
n_per_arm,
power = 0.8,
test_sides = 2
)
rr_minimal(
n_per_arm = NULL,
pi_0 = NULL,
rr_above_1,
power = 0.8,
alpha = 0.05,
test_sides = 2
)
rr_design(
n_per_arm = NULL,
pi_1 = NULL,
pi_0 = NULL,
rr = NULL,
rr_above_1 = NULL,
power = NULL,
alpha = NULL,
test_sides = 2
)
Arguments
- pi_1
A
numeric
vector: the risk of the outcome under treatment- pi_0
A
numeric
vector: the risk of the outcome under control- rr
A
numeric
vector: the relative risk of the outcome under treatment relative to control- power
A
numeric
vector: the statistical power of interest- alpha
A
numeric
vector: Type I Error probability- test_sides
A
numeric
vector: Number of sides for test (1 or 2)- n_per_arm
A
numeric
vector: the sample size in each arm in a 1:1 randomized study- rr_above_1
A
logical
scalar: search for a relative risk above 1 (TRUE
) or below 1 (FALSE
)
Details
The function rr_design
takes in user input, and depending on the
arguments supplied, calls the appropriate function depending on whether the
sample size (rr_n_per_arm
), power (rr_power
), Type I Error
(rr_alpha
), or relative risk (rr_minimal
),
See also
hr_design()
for hazard ratios.
Examples
# Sample Size
rr_design(
pi_0 = 0.08,
rr = 0.8,
power = 0.80,
alpha = 0.025,
test_sides = 1
)
#> n_per_arm n_total
#> 1 4118.085 8236.17
# Power
rr_design(
n_per_arm = 4500,
pi_0 = 0.08,
rr = 0.8,
alpha = 0.025,
test_sides = 1
)
#> [1] 0.8336406
# Type I Error
rr_design(
n_per_arm = 4500,
pi_0 = 0.08,
rr = 0.8,
power = 0.80,
test_sides = 1
)
#> [1] 0.0184443
# Relative Risk: Treatment < Control
rr_design(
n_per_arm = 4500,
pi_0 = 0.08,
rr_above_1 = FALSE,
power = 0.80,
alpha = 0.025,
test_sides = 1
)
#> [1] 0.8083121
# Relative Risk: Treatment > Control
rr_design(
n_per_arm = 4500,
pi_0 = 0.08,
rr_above_1 = TRUE,
power = 0.80,
alpha = 0.025,
test_sides = 1
)
#> [1] 1.209989