n_binom: Calculate sample size for 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_binom(
  p0,
  effect,
  size = 1,
  alpha = 0.05,
  power = 0.9,
  q = 0.5,
  link = c("logit", "identity"),
  two_sided = TRUE
)

Arguments

p0	probability of success in group0
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.
size	number of trials (greater than zero)
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_binom(p0 = 0.5, effect = 0.3)

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