Empirical Power for Non-Inferiority (Normal Outcomes)

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 ntest.
test_req: Integer. Number of required tests that must show non-inferiority (first test_req tests).
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 ntest.
corr: Numeric. Correlation between the tests. Scalar (common correlation), or vector of length ntest*(ntest-1)/2.
t_level: Numeric. Confidence level used for the t-tests (e.g., 0.95 for 95% CI).scalar or vector of length ntest.
conf.level: Numeric. Confidence level for the empirical power estimate

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.

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.

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

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
)
#> Empirical Power Result
#> ----------------------- 
#> Power:       1.0000
#> 95% CI:      [0.9963, 1.0000]
#> Simulations: 1000