sim_power_nbinom: Empirical Power for Negative Binomial Comparison

In ssutil: Sample Size Calculation Tools

Empirical Power for Negative Binomial Comparison

Description

Estimates empirical power to detect a relative risk either above or below a specified boundary, depending on the direction of the alternative hypothesis. Simulates count data with over dispersion, fits a model with glm.nb, and evaluates the power to reject the null hypothesis using a negative binomial model.

Usage

sim_power_nbinom(
  n1,
  n2,
  ir1,
  tm,
  rr,
  boundary,
  dispersion,
  alpha,
  direction = c("less", "greater"),
  nsim,
  conf.level = 0.95
)

Arguments

n1	Integer. Number of participants in group 1.
n2	Integer. Number of participants in group 2.
ir1	Numeric. Incidence rate in group 1.
tm	Numeric. Average exposure time per subject (assumed equal across subjects).
rr	Numeric. True relative risk between groups (group 2 rate = rr × group 1 rate).
boundary	Numeric. Relative risk boundary under the null hypothesis.
dispersion	Numeric. Dispersion parameter (\phi) for the negative binomial distribution.
alpha	Numeric. Type I error rate (two-sided).
direction	= Direction of the alternative hypothesis
nsim	Integer. Number of simulation iterations.
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.

Note

Uses the alternative parameterization of the negative binomial: mu is the mean, and size = 1/dispersion. In glm.nb, dispersion is estimated as theta. The 'boundary' parameter defines the relative risk under the null hypothesis. When rr < 1, rejection occurs if the upper limit of the confidence interval is below the boundary. When rr > 1, rejection occurs if the lower limit is above the boundary.

The alpha parameter is two-sided as it is used to estimate two-sided confidence intervals

Direction of the alternative hypothesis: "less" rejects when the CI upper bound of the simulation is below boundary (e.g. non-inferiority, RR bounded above); "greater" rejects when the CI lower bound of the simulation is above boundary (RR bounded below). Must be specified explicitly.

Author(s)

Chris Gast

John J. Aponte

See Also

empirical_power_result

Examples

sim_power_nbinom(
 n1 = 150, n2 = 150,
 ir1 = 0.55, tm = 1.7,
 rr = 0.6, boundary = 1,
 dispersion = 2,
 alpha = 0.05,
 direction = "less",
 nsim = 1000
)

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