samplingDataCRT (version 1.0)

calcPower.SWD: Power calculation within stepped wedge design model by Hussey et.al or Heo&Kim

Description

Calculation of power for a lmm with cluster as random effect, fixed timepoint effects, but set to null, TP number of timepoints, I number of cluster. The design matrix has to be coded by zeros and ones.

Usage

calcPower.SWD(ThetaEst, alpha = 0.05, Design, sigmaq, tauq, sigmaq.error = NULL, noSub = NULL, time = TRUE, type = "cross-sectional")

Arguments

ThetaEst

expected treatment effect

alpha

singificance level (by default 0.05)

Design

design matrix for a given SWD model

sigmaq

within cluster variance(between subject variance)

tauq

between cluster variance

sigmaq.error

within subject variance/error variance

noSub

number of subjects within each cluster and each timepoint (for all an equal size)

time

a logical (FALSE, if no time trends are expected, otherwise TRUE) is only relevant for evaluation of cross-sectional data

type

is of cross-sectional (by default) or longitudinal assigns the type of data (2 or 3 level nested structure)

Value

Aproximated power of two tailed test, although the design matrix is fractionated, then power is not valid formula used for cross-sectional data provided by Michael A. Hussey and James P. Hughes, Design and analysis of stepped wedge cluster randomized trials, Contemporary Clinical Trials(28),2007, and for longitudinal data by Heo M., Kim N., Rinke ML., Wylie-Rosett J., Sample size determinations for stepped-wedge clinical trials from a three-level data hierarchy perspective, Stat Methods Med Res., 2016

Examples

noCl<-10
noT<-6
switches<-2
DM<-designMatrix(noCl,noT,switches)
sigma.e <- 2
sigma.alpha <- 2   
#Power for cross-sectional SWD design by formula of Hussey&Hughes 
calcPower.SWD(ThetaEst=1,Design=DM, sigmaq=sigma.e^2, tauq=sigma.alpha^2, time=FALSE)
calcPower.SWD(ThetaEst=1,Design=DM, sigmaq=sigma.e^2, tauq=sigma.alpha^2, time=TRUE)
#Power for longitudinal SWD design by formula of Heo&Kim 
DM.new<-NULL
for(i in 1:dim(DM)[2]){
DM.new<-cbind(DM.new,DM[,i], DM[,i])
}
s.e <- sqrt(7/10)
s <- sqrt(2/10)
s.a <- sqrt(1/10 )
K<- 10 #number of participants within each 'cell'
calcPower.SWD(ThetaEst=1, Design=DM.new, s.a^2, s^2, s.e^2,  noSub=K,  type="longitudinal")