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Last data update: 2014.03.03

R: Linear mixed model sample size calculations.

edland.linear.power

R Documentation

Linear mixed model sample size calculations.

Description

This function performs the sample size calculation for a linear mixed model with random slope.

Usage

edland.linear.power(n = NULL, delta = NULL, t = NULL, sig2.s = 0,
  sig2.e = 1, sig.level = 0.05, power = NULL,
  alternative = c("two.sided", "one.sided"), tol = .Machine$double.eps^2)

Arguments

`n`	sample size per group
`delta`	group difference in slopes
`t`	the observation times
`sig2.s`	variance of random slope
`sig2.e`	residual variance
`sig.level`	type one error
`power`	power
`alternative`	one- or two-sided test
`tol`	numerical tolerance used in root finding.

Details

This function will also provide sample size estimates for linear mixed models with random intercept only simply by setting sig2.s = 0

Value

The number of subject required per arm to attain the specified power given sig.level and the other parameter estimates.

Author(s)

Michael C. Donohue, Steven D. Edland

References

Edland, S.D. (2009) Which MRI measure is best for Alzheimer's disease prevention trials: Statistical considerations of power and sample size. Joint Stat Meeting Proceedings. 4996-4999.

Examples


## Not run: 
browseVignettes(package = "longpower")

## End(Not run)
# Reproduces the table on page 29 of Diggle et al
n = 3
t = c(0,2,5)
rho = c(0.2, 0.5, 0.8)
sigma2 = c(100, 200, 300)
tab = outer(rho, sigma2, 
      Vectorize(function(rho, sigma2){
        ceiling(edland.linear.power(
          delta=0.5,
          t=t,
          sig2.e=sigma2*(1-rho),
          alternative="one.sided",
          power=0.80)$n)}))
colnames(tab) = paste("sigma2 =", sigma2)
rownames(tab) = paste("rho =", rho)
tab

# An Alzheimer's Disease example using ADAS-cog pilot estimates
t = seq(0,1.5,0.25)
n = length(t)

edland.linear.power(delta=1.5, t=t, sig2.s = 24, sig2.e = 10, sig.level=0.05, power = 0.80)