Analysis of Multisite Randomised Education Trials using Multilevel Model.

Description

mstFREQ performs analysis of multisite randomised education trial using multilevel model within the frequentist framework.

Usage

1
mstFREQ(formula, random, intervention, nPerm = NULL, data, nBoot = NULL)

Arguments

formula

specifies the model to be analysed. It is of the form y ~ x1+x2+....

random

a string variable specifying the "clustering variable" as contained in the data.

intervention

a string variable specifying the intervention variable in the formula.

nPerm

specifies number of perumation under the null hypothesis. Default is NULL.

data

specifies a data frame containing the data to be analysed.

nBoot

specifies number of bootstrap samples for non-parametric bootstrap confidence interval. Default is NULL.

Value

S3 object; a list consisting of

Examples

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if(interactive()){

data(mstData)

########################################################
## MLM analysis of multisite trials + 1.96SE ##
########################################################

output1 <- mstFREQ(Posttest~ Intervention+Prettest,random="School",
		intervention="Intervention",data=mstData)


### Fixed effects
beta <- output1$Beta
beta

### Effect size
ES1 <- output1$ES$Intervention1
ES1

## Covariance matrix
covParm <- output1$covParm
covParm

### plot random effects for schools

plotObject(analyticObject=output1)

###############################################
## MLM analysis of multisite trials          ##	 
## with bootstrap confidence intervals       ##
###############################################

output2 <- mstFREQ(Posttest~ Intervention+Prettest,random="School",
		intervention="Intervention",nBoot=1000,data=mstData)


### Effect size

ES2 <- output2$ES
ES2

### plot bootstrapped values 

plotObject(analyticObject=output2, group=1)

#######################################################################
## MLM analysis of mutltisite trials with permutation p-value##
#######################################################################

output3 <- mstFREQ(Posttest~ Intervention+Prettest,random="School",
		intervention="Intervention",nPerm=1000,data=mstData)


#### plot permutated values 

plotObject(analyticObject=output3, group=1)


}

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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