R: Sample size calculations for linear mixed models of rate of...
lmmpower
R Documentation
Sample size calculations for linear mixed models of rate of change based on
lmer, lme, or gee "placebo" pilot estimates.
Description
These functions compute sample size for linear mixed models based on the
formula due to Diggle (2002) or Liu and Liang (1997). These formulae are
expressed in terms of marginal model or Generalized Estimating Equations
(GEE) parameters. These functions translate pilot mixed effect model
parameters (e.g. random intercept and/or slope, fixed effects, etc.) into
marginal model parameters so that either formula can be applied to
equivalent affect. Pilot estimates are assumed to be from an appropriate
"placebo" group and the parameter of interest is assumed to be the rate of
change over time of the outcome.
sample size per group
of a mixed-effects model object to placebo data assumed to have either a
random intercept, or a random intercept and random effect for time (slope);
and fixed effect representing the rate of change in a placebo group.
parameter
the name or position
of the rate of change parameter of interest, e.g. ("time",
"t", or 2 if it is the second specified fixed effect).
pct.change
the percent change
in the pilot estimate of the parameter of interest (beta, the
placebo/null effect)
delta
the change in the pilot estimate
of the parameter of interest, computed from pct.change if left
missing.
t
vector of time points
sig.level
Type I error
power
power
alternative
"two.sided" or "one.sided"
beta
pilot estimate of the placebo
effect (slope or rate of change in the outcome)
beta.CI
95% confidence limits of
the pilot estimate of beta
delta.CI
95% confidence limits of
the effect size
sig2.i
pilot estimate of variance
of random intercept
sig2.s
pilot estimate of variance
of random slope
sig2.e
pilot estimate of residual
variance
cov.s.i
pilot estimate of
covariance of random slope and intercept
R
pilot estimate of a marginal
model working correlation matrix
method
the formula to use. Defaults
to "diggle" for Diggle et al (2002). Alternatively "liuliang"
can be selected for Liu & Liang (1997).
tol
numerical tolerance used in root finding.
...
other arguments
Details
Any parameters not explicitly stated are extracted from the fitted
object.
Value
An object of class power.htest giving the calculated sample
size, N, per group and other parameters.
Author(s)
Michael C. Donohue
References
Diggle P.J., Heagerty P.J., Liang K., Zeger S.L. (2002)
Analysis of longitudinal data. Second Edition. Oxford Statistical
Science Series.
Liu, G., and Liang, K. Y. (1997) Sample size calculations for studies with
correlated observations. Biometrics, 53(3), 937-47.
See Also
liu.liang.linear.powerdiggle.linear.power
Examples
## Not run:
browseVignettes(package = "longpower")
## End(Not run)
lmmpower(delta=1.5, t = seq(0,1.5,0.25),
sig2.i = 55, sig2.s = 24, sig2.e = 10, cov.s.i=0.8*sqrt(55)*sqrt(24), power = 0.80)
lmmpower(n=208, t = seq(0,1.5,0.25),
sig2.i = 55, sig2.s = 24, sig2.e = 10, cov.s.i=0.8*sqrt(55)*sqrt(24), power = 0.80)
lmmpower(beta = 5, pct.change = 0.30, t = seq(0,1.5,0.25),
sig2.i = 55, sig2.s = 24, sig2.e = 10, cov.s.i=0.8*sqrt(55)*sqrt(24), power = 0.80)
## Not run:
library(lme4)
fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
lmmpower(fm1, pct.change = 0.30, t = seq(0,9,1), power = 0.80)
library(nlme)
fm2 <- lme(Reaction ~ Days, random=~Days|Subject, sleepstudy)
lmmpower(fm2, pct.change = 0.30, t = seq(0,9,1), power = 0.80)
# random intercept only
fm3 <- lme(Reaction ~ Days, random=~1|Subject, sleepstudy)
lmmpower(fm3, pct.change = 0.30, t = seq(0,9,1), power = 0.80)
library(gee)
fm4 <- gee(Reaction ~ Days, id = Subject,
data = sleepstudy,
corstr = "exchangeable")
lmmpower(fm4, pct.change = 0.30, t = seq(0,9,1), power = 0.80)
## End(Not run)