sampleSizeRandomized: Sample Size Calculation for Completely Randomized...

In sizepower: Sample Size and Power Calculation in Micorarray Studies

Description

For any specified power, this routine computes the required sample size n for completely randomized designs in which differential expression between n treatment units and n control units is of interest. The total number of experimental units for the study is 2n.

Usage

  sampleSize.randomized(ER0, G0, power, absMu1, sigmad)

Arguments

ER0	mean number of false positives.
G0	anticipated number of genes in the experiment that are not differentially expressed.
power	specified power level for an individual gene, which represents the expected proportion of differentially expressed genes that will be declared as such by the tests.
absMu1	absolute mean difference in log-expression between treatment and control conditions as postulated under the alternative hypothesis H1.
sigmad	anticipated standard deviation of the difference in log-expression between treatment and control conditions. The relation between the standard deviation of the difference (sigmad) and the experimental error standard deviation (sigma) is sigmad=sqrt(2)/sigma.

Value

n	sample size for each group.
d	statistical difference between treatment and control conditions under H1 (i.e. d=absMu1/sigmad).

Note

Examples and explainations can be found in http://www.biostat.harvard.edu/people/faculty/mltlee/pdf/Web-sampsize-trt-cont-050511r.pdf.

Author(s)

Weiliang Qiu ([e-mail]), Mei-Ling Ting Lee ([e-mail]), George Alex Whitmore ([e-mail])

References

Lee, M.-L. T. (2004). Analysis of Microarray Gene Expression Data. Kluwer Academic Publishers, ISBN 0-7923-7087-2.

Lee, M.-L. T., Whitmore, G. A. (2002). Power and sample size for DNA microarray studies. Statistics in Medicine, 21:3543-3570.

See Also

power.randomized, power.matched, power.multi, sampleSize.matched

Examples

  sampleSize.randomized(ER0=1, G0=2000, power=0.9, absMu1=1, sigmad=0.566)

Example output

$n
[1] 8

$d
[1] 1.766784

sizepower documentation built on Nov. 8, 2020, 5:26 p.m.