sizepower (version 1.42.0)

power.multi: Power Calculations for Multiple Treatments Design with an Isolated Treatment Effect in Microarray 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.

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)