View source: R/genodds.power.R
genodds.power | R Documentation |
Provides power analysis for Agresti's Generalized Odds Ratios.
genodds.power( p0, p1, N = NULL, power = NULL, alpha = 0.05, ties = "split", w = c(0.5, 0.5), direction = "two.sided" )
p0 |
A numeric vector contianing the probabilities in control group. |
p1 |
A numeric vector contianing the probabilities in treatment group. |
N |
A numeirc vector containing total sample sizes. |
power |
A numeric vector containing required total sample size. |
alpha |
Type 1 error. |
ties |
A string specifying how ties should be treated. Should be equal to "split" 0.5 for WMW Odds, or "drop" for Agresti's GenOR. |
w |
A numeric vector of length 2 specifying the relative weighting of sample size between treatment groups. |
direction |
Direction for hypothesis test.
Must be one of |
See genodds
for explanation of generalized odds ratios.
N
provides the total sample size.
Sample size per group can be calculated by N*w/sum(w)
.
When power
is supplied, if no sufficient sample size is
found then this function will return Inf
.
power
is suppliedA numeric vector containing required sample sizes to achieve specified powers.
N
is suppliedA numeric vector containing power at specified sample sizes.
O'Brien, R. G., & Castelloe, J. (2006, March). Exploiting the link between the Wilcoxon-Mann-Whitney test and a simple odds statistic. In Thirty-first Annual SAS Users Group International Conference.
# Provide theoretical distributions of outcomes for each group # Distributions taken from Lees et. al. (2010). See ?alteplase for a citation. p0 <- c(0.224,0.191,0.082,0.133,0.136,0.043,0.191) p1 <- c(0.109,0.199,0.109,0.120,0.194,0.070,0.200) # Calculate sample size required to achieve 80% and 90% # power for these distributions genodds.power(p0,p1,power=c(0.8,0.9)) # genodds.power suggests a total sample size of 619 for 80% power. # Round up to 620 for even sample size per group # Confirm these sample sizes lead to 80% and 90% power genodds.power(p0,p1,N=c(620,830))
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