Power and Sample Size for Ordinal Response
popower computes the power for a two-tailed two sample comparison
of ordinal outcomes under the proportional odds ordinal logistic
model. The power is the same as that of the Wilcoxon test but with
ties handled properly.
posamsize computes the total sample size
needed to achieve a given power. Both functions compute the efficiency
of the design compared with a design in which the response variable
popower(p, odds.ratio, n, n1, n2, alpha=0.05) "print"(x, ...) posamsize(p, odds.ratio, fraction=.5, alpha=0.05, power=0.8) "print"(x, ...)
a vector of marginal cell probabilities which must add up to one.
ith element specifies the probability that a patient will be in response level
i, averaged over the two treatment groups.
- the odds ratio to be able to detect. It doesn't matter which group is in the numerator.
total sample size for
popower. You must specify either
n2. If you specify
n2are set to
popower, the number of subjects in treatment group 1
popower, the number of subjects in group 2
- type I error
- an object created by
posamsize, the fraction of subjects that will be allocated to group 1
posamsize, the desired power (default is 0.8)
a list containing
eff(relative efficiency) for
popower, or containing
Whitehead J (1993): Sample size calculations for ordered categorical data. Stat in Med 12:2257--2271.
Julious SA, Campbell MJ (1996): Letter to the Editor. Stat in Med 15: 1065--1066. Shows accuracy of formula for binary response case.
#For a study of back pain (none, mild, moderate, severe) here are the #expected proportions (averaged over 2 treatments) that will be in #each of the 4 categories: p <- c(.1,.2,.4,.3) popower(p, 1.2, 1000) # OR=1.2, total n=1000 posamsize(p, 1.2) popower(p, 1.2, 3148)