sim_power_nbinom {ssutil} | R Documentation |
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,
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 ( |
alpha |
Numeric. Type I error rate (two-sided). |
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
Author(s)
Chris Gast
John J. Aponte
See Also
Examples
sim_power_nbinom(
n1 = 150, n2 = 150,
ir1 = 0.55, tm = 1.7,
rr = 0.6, boundary = 1,
dispersion = 2,
alpha = 0.05,
nsim = 1000
)