powerMulti: Power Calculations for Multiple Treatments Design with an...

In sizepower: Sample Size and Power Calculation in Micorarray Studies

Description

Assume numTrt treatment conditions are being studied in either a completely randomized or randomized block design. Under the alternative hypothesis H1, one treatment is distinguished from the other numTrt - 1 treatments by exhibiting differential expression for the gene. This computer routine calculates the individual power value for the design. This power value is the expected fraction of truly differentially expressed genes that will be correctly declared as differentially expressed by the tests.

Usage

  power.multi(ER0, G0, numTrt, absMu1, sigma, n)

Arguments

ER0	mean number of false positives.
G0	anticipated number of genes in the experiment that are not differentially expressed.
numTrt	total number of treatment conditions.
absMu1	the absolute difference in expression between the distinguished treatment and the other treatments on the log-intensity scale.
sigma	anticipated experimental error standard deviation of the difference in log-expression between treatments.
n	the sample size for each group.

Value

power	power.
psi1	non-centrality parameter.

Note

Examples and explainations can be found in http://www.biostat.harvard.edu/people/faculty/mltlee/pdf/Web-power-isolated050510.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, sampleSize.randomized, sampleSize.matched

Examples

  power.multi(ER0=2, G0=10000, numTrt=6, absMu1=0.585, sigma=0.3, n=8)

Example output

$power
[1] 0.6990298

$psi1
[1] 25.35

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