Simulate Power to Rank the Best Group Using Binomial Outcomes

Estimates the empirical power to rank the most promising group as the best, based on binomial outcomes, via simulation.

Usage

sim_power_best_bin_rank(
  noutcomes,
  p1,
  dif,
  weights,
  ngroups,
  npergroup,
  nsim,
  conf.level = 0.95
)

Arguments

noutcomes: Integer. Number of outcomes to evaluate.
p1: Numeric. Event probability in the best group (scalar or vector of length noutcomes).
dif: Numeric. Difference between the best group and the rest (scalar or vector of length noutcomes).
weights: Numeric vector. Weights for each outcome. If scalar, applied equally.
ngroups: Integer. Number of groups.
npergroup: Integer or vector. Sample size 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 assumed to follow an independent binomial distribution. The best group is defined as having a probability at least dif higher than the other groups. The function sums weighted ranks across multiple outcomes to determine the top group.

If multiple outcomes are defined, weights can be applied to prioritize some outcomes over others. Weights are automatically scaled to sum 1. The group with the lowest total rank is considered the best.

Examples

  sim_power_best_bin_rank(
  noutcomes = 2,
  p1 = 0.80,
  dif = 0.15,
  weights = 1,
  ngroups = 3,
  npergroup = 30,
  nsim = 1000,
  conf.level = 0.95)
#> Empirical Power Result
#> ----------------------- 
#> Power:       0.8820
#> 95% CI:      [0.8604, 0.9013]
#> Simulations: 1000