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

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

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

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.

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
)
#> Empirical Power Result
#> ----------------------- 
#> Power:       0.7370
#> 95% CI:      [0.7085, 0.7641]
#> Simulations: 1000