sim_power_best_norm_rank: Simulate Power to Select Best Group by Ranks (Normal...

In ssutil: Sample Size Calculation Tools

Simulate Power to Select Best Group by Ranks (Normal Outcomes)

Description

Estimates the empirical power to identify the most promising group as best, using weighted ranks across outcomes, assuming normally distributed outcomes.

Usage

sim_power_best_norm_rank(
  noutcomes,
  sd,
  dif,
  weights,
  ngroups,
  npergroup,
  nsim,
  conf.level = 0.95
)

Arguments

noutcomes	Integer. Number of outcomes to evaluate.
sd	Numeric vector. Standard deviations for each outcome.
dif	Numeric vector. Difference in means between the best and other groups.
weights	Numeric vector. Weights per outcome.
ngroups	Integer. Number of groups.
npergroup	Integer or vector. Number of subjects per group.
nsim	Integer. Number of simulations.
conf.level	Numeric. Confidence level for the empirical power estimate

Details

Each outcome is independent and normally distributed. The most promising group is assumed to have a mean at least dif higher than the others. Ranks are weighted and summed per group across outcomes.

If weights is specified, it is internally scaled to sum to 1. The most promising group is always considered to be the first group.

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.

See Also

empirical_power_result

Examples

sim_power_best_norm_rank(
  noutcomes = 3,
  sd = c(1, 0.8, 1.5),
  dif = c(0.2, 0.15, 0.3),
  weights = c(0.5, 0.3, 0.2),
  ngroups = 3,
  npergroup = c(30, 25, 25),
  nsim = 1000
)

ssutil documentation built on Sept. 18, 2026, 1:06 a.m.