Simulations for Non-inferiority trials

The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

Introduction

Non-inferiority tests are used in clinical trials when the objective is to demonstrate that a new treatment is not significantly worse than a reference treatment by more than a pre-specified margin.

In designs involving multiple outcomes, it may be required that some outcomes meet strict non-inferiority criteria (e.g., the primary outcomes) while others can meet more flexible conditions (e.g., at least two out of three secondary outcomes).

This vignette shows how to use the sim_power_ni_normal() function from the ssutil package to estimate power in such a design.

Example

What is the empirical power of a trial that wants to evaluate non-inferiority of the immune response for a vaccine against 7 serotypes? Non-inferiority is achieved if the geometric mean ratio between the candidate vaccine and the reference is greater than 0.5 for the first two antigens, and for three of the other five.

This example simulates 1000 trials with 125 participants per group, 7 independent outcomes, a standard deviation of 0.8 on the log10 scale for all outcomes, and a common non-inferiority margin equivalent to log10(2/3). Non-inferiority is required in the first two outcomes, and in at least three of the remaining five.

library(ssutil)
set.seed(123)

res <- sim_power_ni_normal(
  nsim = 1000,
  npergroup = 125,
  ntest = 7,
  ni_limit = log10(2/3),
  test_req = 2,
  test_opt = 3,
  sd = 0.8,
  corr = 0,
  t_level = 0.05
)

res
#> Empirical Power Result
#> ----------------------- 
#> Power:       0.9120
#> 95% CI:      [0.8927, 0.9288]
#> Simulations: 1000

The result shows the proportion of simulations in which the full non-inferiority rule was satisfied.

library(ssutil)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.