sim_power_ni_normal {ssutil} | R Documentation |
Empirical Power for Non-Inferiority (Normal Outcomes)
Description
Estimates empirical power to declare non-inferiority between two groups across multiple outcomes using t-tests. Simulates normally distributed data under the null (no difference) and applies non-inferiority rules based on user-defined required and optional tests.
Usage
sim_power_ni_normal(
nsim,
npergroup,
ntest,
ni_limit,
test_req,
test_opt,
sd,
corr = 0,
t_level = 0.95,
conf.level = 0.95
)
Arguments
nsim |
Integer. Number of simulations to perform. |
npergroup |
Integer. Number of observations per group. |
ntest |
Integer. Number of tests (outcomes) to compare. |
ni_limit |
Numeric. Limit to declare non-inferiority. Can be a scalar or vector of length |
test_req |
Integer. Number of required tests that must show non-inferiority (first |
test_opt |
Integer. Number of optional tests that must also show non-inferiority from the remaining tests. |
sd |
Numeric. Standard deviation(s) of the outcomes. Scalar or vector of length |
corr |
Numeric. Correlation between the tests. Scalar (common correlation), or vector of length |
t_level |
Numeric. Confidence level used for the t-tests (e.g., 0.95 for 95% CI).scalar or vector of length |
conf.level |
Numeric. Confidence level for the empirical power estimate |
Details
A test is considered non-inferior if the lower bound of its confidence interval is greater
than the specified non-inferiority limit. Overall non-inferiority is declared if all
test_req
and at least test_opt
of the remaining tests are non-inferior.
Value
An S3 object of class empirical_power_result
, which contains
the estimated empirical power and its confidence interval. The object can
be printed, formatted, or further processed using associated S3 methods.
See also empirical_power_result
.
Note
If only one test is used, correlation is ignored.
Use correlation 0 for independent outcomes
When using a correlation vector, it must match the number of test pairs:
ntest*(ntest-1)/2
, in this order: (1,2), (1,3), ..., (1,ntest), (2,3), ..., (ntest-1,ntest).
The covariance matrix is derived from the correlation matrix and the standard deviations.
For example: with ntest = 3
and corr = c(0.2, 0.3, 0.4)
, the resulting correlation matrix is:
[,1] | [,2] | [,3] | |
[1, ] | 1 | 0.2 | 0.3 |
[2, ] | 0.2. | 1 | 0.4 |
[3, ] | 0.3. | 0.4 | 1 |
See Also
Examples
sim_power_ni_normal(
nsim = 1000,
npergroup = 250,
ntest = 7,
ni_limit = log10(2/3),
test_req = 2,
test_opt = 3,
sd = 0.4,
corr = 0,
t_level = 0.05
)