power.dunnett.test {PMCMRplus}

Power Calculations for Balanced Dunnett's Many-to-One Comparison Test

Description

Compute average per-pair power of Dunnetts's multiple comparison test with one control.

Usage

power.dunnett.test(n, groups, delta, within.var, sig.level = 0.05)

Arguments

n

Number of observations (per group)

groups

Number of groups (including control)

delta

true difference in means

within.var

Within group variance

sig.level

Significance level (Type I error probability)

Details

The function has implemented the following Eq. to estimate average per-pair power for two-sided tests:

1 - \beta = 1 - t( T_{\alpha \rho v}, v, \mathrm{ncp}) + t(-T_{\alpha \rho v}, v, \mathrm{ncp}),

with T_{\alpha \rho v} the two-sided \alpha quantile of the multivariate t-distribution, with v = k (n - 1) degree of freedom, k the number of groups and correlation matrix \rho_{ij} = 0.5 ~ (i \neq j).

The non-centrality parameter for the non-central student t-distribution is

\mathrm{ncp} = |\Delta| / \sqrt{s_{\mathrm{in}}^2 ~ 2 / n }.

Value

Object of class ‘power.htest’, a list of the arguments (including the computed one) augmented with method and note elements.

Note

The results for power are seed depending.

Source

The Eqs. were taken from Lecture 5, Determining Sample Size, Statistics 514, Fall 2015, Purdue University, IN, USA.

See Also

TDist qmvt powerMCTests

Examples

set.seed(113)
power.dunnett.test(n = 9, groups = 5, delta = 30,
 within.var = 333.7)

## compare with t-test, bonferroni corrected
power.t.test(n = 9, delta = 30, sd = sqrt(333.7),
sig.level = 0.05 / 4)

## Not run: 
## asymptotic Monte-Carlo power analysis
 set.seed(113)
 powerMCTests(mu = c(rep(0,4), 30), n = 9,
 parms = list(mean = 0, sd = sqrt(333.7)),
 test = "dunnettTest", alternative = "two.sided")

## End(Not run)