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
is continuous.
print
methods exist for both functions. Any of the
input arguments may be vectors, in which case a vector of powers or
sample sizes is returned. These functions use the methods of
Whitehead (1993).
popower(p, odds.ratio, n, n1, n2, alpha=0.05) ## S3 method for class 'popower': print(x, ...) posamsize(p, odds.ratio, fraction=.5, alpha=0.05, power=0.8) ## S3 method for class 'posamsize': print(x, ...)
i
th element specifies the probability that a patient will be in response level
i
, averaged over the two treatment groups.
popower
. You must specify either
n
or
n1
and
n2
. If you specify
n
,
n1
and
n2
are set to
n/2
.
popower
, the number of subjects in treatment group 1
popower
, the number of subjects in group 2
popower
or
posamsize
posamsize
, the fraction of subjects that will be allocated to group 1
posamsize
, the desired power (default is 0.8)
power
and
eff
(relative efficiency) for
popower
,
or containing
n
and
eff
for
posamsize
.
Frank Harrell
Department of Biostatistics
Vanderbilt University School of Medicine
f.harrell@vanderbilt.edu
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)