power.ladesign: Power of k-sample rank test under Lehmann alternative

In clinfun: Clinical Trial Design and Data Analysis Functions

Power of k-sample rank test under Lehmann alternative

Description

Functions to calculate the power of rank tests for animal studies.

Usage

  power.ladesign(gsize, odds.ratio, sig.level = 0.05, statistic =
     c("Kruskal-Wallis", "Jonckheere-Terpstra"), alternative =
     c("two.sided", "one.sided"), nrep=1e+6) 
  ## S3 method for class 'ladesign'
print(x,...)

Arguments

gsize	sample size of the k (= length of vector) groups.
odds.ratio	odds ratio parameters for the k-1 groups. The first group is considered the control.
sig.level	the significance level of the test (default = 0.05)
statistic	the test statistic for the k-group comparison. Is one of Kruskal-Wallis (default) or Jonckeere-Terpstra.
alternative	one- or two-sided test. Valid only for the Jonckheere-Terpstra test.
nrep	number of reps (default 1 million) for Monte Carlo.
x	object of class ladesign returned by power.ladesign
...	arguments to be passed on left for S3 method consistency.

Details

Although the power for Jonckheere-Terpstra test is calculated for any set of odds ratio, the test is meant for monotone alternative. Thus it is preferable to specify odds ratios that are monotonically increasing with all values larger than 1 or decreasing with all values smaller than 1.

Value

returns a list with objects group.size, odds.ratio, statistic, sig.level and power. The "print" method formats the output.

References

Heller G. (2006). Power calculations for preclinical studies using a K-sample rank test and the Lehmann alternative hypothesis. Statistics in Medicine 25, 2543-2553.

Examples

  power.ladesign(c(9,7), 4, statistic="K")
  power.ladesign(c(9,7,9), c(2,4), statistic="J")
  power.ladesign(c(9,7,9), c(2,4), statistic="J", alt="o")

clinfun documentation built on Oct. 20, 2023, 1:07 a.m.