n_negbinom: Calculate sample size for negative binomial distribution

In skewsamp: Estimate Sample Sizes for Group Comparisons with Skewed Distributions

Description

Estimation of required sample size as given by Cundill & Alexander (2015).

Usage

n_negbinom(
  mean0,
  effect,
  dispersion0,
  dispersion1 = dispersion0,
  alpha = 0.05,
  power = 0.9,
  q = 0.5,
  link = c("log", "identity"),
  two_sided = TRUE
)

Arguments

mean0	Mean in control group
effect	Effect size, 1 - (μ_1 / μ_0), where μ_0 is the mean in the control group (mean0) and μ_1 is the mean in the treatment group.
dispersion0	Dispersion parameter in control group
dispersion1	Dispersion parameter in treatment group. Defaults to shape0, because GLM assumes equal shape across groups.
alpha	Type I error rate
power	1 - Type II error rate
q	Proportion of observations allocated to the control group
link	Link function to use. Currently implement: 'log' and 'identity'
two_sided	logical, if TRUE the sample size will be calculated for a two-sided test. Otherwise, the sample size will be calculated for a one-sided test.

Value

Returns an object of class "sample_size". It contains the following components:

N	the total sample size
n0	sample size in Group 0 (control group)
n1	sample size in Group 1 (treatment group)
two_sided	logical, TRUE, if the estimated sample size refers to a two-sided test
alpha	type I error rate used in sample size estimation
power	target power used in sample size estimation
effect	effect size used in sample size estimation
effect_type	short description of the type of effect size
comment	additional comment, if there is any
call	the matched call.

References

Cundill, B., & Alexander, N. D. E. (2015). Sample size calculations for skewed distributions. BMC Medical Research Methodology, 15(1), 1–9. https://doi.org/10.1186/s12874-015-0023-0

Examples

n_negbinom(mean0 = 71.4, effect = 0.7, dispersion0 = 0.33,
           alpha = 0.05, power = 0.9)

skewsamp documentation built on Dec. 17, 2021, 1:07 a.m.