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Calculate Sample Size Needed to Compare k Means: 1-Way ANOVA Pairwise, 1-Sided

This calculator is useful for testing the means of several groups. The statistical model is called an Analysis of Variance, or ANOVA model. This calculator is for the particular situation where we wish to make pairwise comparisons between groups. That is, we compare two groups at a time, and we make several of these comparisons. For example, suppose we want to compare the means of three groups called foo, bar, and ack. These groups may represent groups of people that have been exposed to three different medical procedures, marketing schemes, etc. The complete list of pairwise comparisons are foo vs. bar, foo vs. ack, and bar vs. ack. In more general terms, we may have kk groups, meaning there are a total of K≡(k2)=k(k−1)/2K\equiv\binom{k}{2}=k(k-1)/2 possible pairwise comparisons. When we test τ≤K\tau\le K of these pairwise comparisons, we have τ\tau hypotheses of the form
H0:μA=μBH_0:\mu_A=\mu_B
H1:μA<μBH_1:\mu_A\lt\mu_B
or
H0:μA=μBH_0:\mu_A=\mu_B
H1:μA<μBH_1:\mu_A\lt\mu_B
where μA\mu_A and μB\mu_B represent the means of two of the kk groups, groups 'A' and 'B'. We'll compute the required sample size for each of the τ\tau comparisons, and total sample size needed is the largest of these.

Formulas

This calculator uses the following formulas to compute sample size and power, respectively: nA=(σA2+σB2/κ)(z1−α/τ+z1−βμA−μB)2n_A=\left(\sigma_A^2+\sigma_B^2/\kappa\right)\left(\frac{z_{1-\alpha/\tau}+z_{1-\beta}}{\mu_A-\mu_B}\right)^2
nB=κnAn_B=\kappa n_A
1−β=Φ(∣μA−μB∣nAσA2+σB2/κ−z1−α/τ)1-\beta=\Phi\left(\frac{|\mu_A-\mu_B|\sqrt{n_A}}{\sqrt{\sigma_A^2+\sigma_B^2/\kappa}}-z_{1-\alpha/\tau}\right)
where κ=nA/nB\kappa=n_A/n_B is the matching ratio σ\sigma is standard deviation σA\sigma_A is standard deviation in Group "A" σB\sigma_B is standard deviation in Group "B" Φ\Phi is the standard Normal distribution function Φ−1\Phi^{-1} is the standard Normal quantile function α\alpha is Type I error τ\tau is the number of comparisons to be made β\beta is Type II error, meaning 1−β1-\beta is power

R Code

1muA=132.86
2muB=127.44
3kappa=2
4sdA=15.34
5sdB=18.23
6tau=1
7alpha=0.05
8beta=0.20
9(nA=(sdA^2+sdB^2/kappa)*((qnorm(1-alpha/tau)+qnorm(1-beta))/(muA-muB))^2)
10ceiling(nA) # 85
11z=(muA-muB)/sqrt(sdA^2+sdB^2/kappa)*sqrt(nA)
12(Power=pnorm(z-qnorm(1-alpha/tau)))
13## Note: Rosner example on p.303 is for 2-sided test.
14## These formulas give the numbers in that example
15## after dividing alpha by 2, to get 2-sided alpha.
16## Also, we don't yet have an example using tau!=1.
17## If you'd like to contribute one please let us know!

References

Rosner B. 2010. Fundamentals of Biostatistics. 7th Ed. Brooks/Cole. page 302 and 303.

Chow S, Shao J, Wang H. 2008. Sample Size Calculations in Clinical Research. 2nd Ed. Chapman & Hall/CRC Biostatistics Series. page 58.