Simulate Power to Select Best Group (Normal Outcomes) — sim_power_best

Estimates the empirical power to identify the most promising group as the best, when outcomes are normally distributed and independent.

Usage

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

Arguments

noutcomes: Integer. Number of outcomes to evaluate.
sd: Numeric vector. Standard deviations for each outcome. Can be a single value.
dif: Numeric vector. Difference in means between the best and the other groups.
ngroups: Number of groups to compare.
npergroup: Number of subjects per group. Can be scalar or vector of length ngroups.
nsim: Integer. Number of simulations to perform.
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

The best group (group 1) is assumed to have mean 0, and the rest of the groups have mean -dif.

Multiple outcomes can be evaluated simultaneously. The power is estimated as the proportion of simulations where the most promising group is the best in all outcomes.

The number of subjects per group can be the same or specified per group. In either case, the first group is assumed to be the most promising.

Examples

  sim_power_best_normal(
   noutcomes = 2,
   sd = c(1, 1.2),
   dif = c(0.2, 0.25),
   ngroups = 3,
   npergroup = c(30, 25, 25),
   nsim = 1000
  )
#> Empirical Power Result
#> ----------------------- 
#> Power:       0.4300
#> 95% CI:      [0.3991, 0.4614]
#> Simulations: 1000