n_binom {skewsamp}

Calculate sample size for binomial distribution

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 - (\mu_1 / \mu_0), where \mu_0 is the mean in the control group (mean0) and \mu_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)