gsDesign (version 2.8-7)

nNormal: Normal distribution sample size (2-sample)

Description

nNormal() computes a fixed design sample size for comparing 2 means where variance is known. T The function allows computation of sample size for a non-inferiority hypothesis. Note that you may wish to investigate other R packages such as the pwr package which uses the t-distr

Usage

nNormal(delta1=1,sigma=1.7,sigalt=NULL,alpha=.025,beta=.1,ratio=1, sided=1,
n=NULL,delta0=0)

Arguments

delta1

difference between sample means under the alternate hypothesis.

delta0

difference between sample means under the null hypothesis; normally this will be left as the default of 0.

ratio

randomization ratio of experimental group compared to control.

sided

1 for 1-sided test (default), 2 for 2-sided test.

sigma

Standard deviation for the control arm.

sigalt

Standard deviation of experimental arm; this will be set to be the same as the control arm with the default of NULL.

alpha

type I error rate. Default is 0.025 since 1-sided testing is default.

beta

type II error rate. Default is 0.10 (90% power). Not needed if n is provided.

n

Sample size; may be input to compute power rather than sample size. If NULL (default) then sample size is computed.

Value

• If n is NULL (default), total sample size (2 arms combined) is computed. Otherwise, power is c

Details

nNormal() computes sample size for comparing two normal means when the variance for observations in

References

Lachin JM (1981), Introduction to sample size determination and power analysis for clinical trials. Controlled Clinical Trials 2:93-113. Snedecor GW and Cochran WG (1989), Statistical Methods. 8th ed. Ames, IA: Iowa State University Press.

See Also

gsDesign package overview

Examples

# EXAMPLES
# equal variances
nNormal(delta1=.5,sigma=1.1,alpha=.025,beta=.2)
# unequal variances
nNormal(delta1=.5,sigma=1.1,sigalt=2,alpha=.025,beta=.2)
# unequal sample sizes
nNormal(delta1=.5,sigma=1.1,alpha=.025,beta=.2, ratio=2)
# non-inferiority assuming a better effect than null
nNormal(delta1=.5,delta0=-.1,sigma=1.2)