clinDR (version 2.4.1)

FixedMean: Fixed means (proportions) random data constructor for emaxsim for continuous or binary data

Description

Creates a list object that contains inputs and a function to create simulated data sets with a common mean (proportion) for use in emaxsim with normal or continuous data

Usage

FixedMean(n, doselev, meanlev, resSD, parm = NULL, binary=FALSE)

Value

A list of length 2. The first element is itself a list named genP that contains named elments n, resSD, doselev, dose, parm, binary, and the element meanlev, which is specific to FixedMean. The second element is a function named genFun that takes genP as input and returns a list with named elements meanlev, parm, resSD, y.

Arguments

n

Sample size for each dose group

doselev

Dose levels (including 0 for placebo) in the study corresponding to n. Must be in increasing order.

meanlev

Mean response at each doselev. For binary data, these are the proportion of responders (no logit transformation).

resSD

Standard deviation for residuals within each dose group (assumed common to all dose groups)

parm

Population parameters that are saved for later reference, but are not used when creating simulated data. parm can contain parameters for a 3- or 4- parameter Emax model that generated meanlev. They should be stored in the order given in emaxfun. Default is NULL.

binary

Normal data with homogeneous variance are generated unless binary is TRUE, and then means are interpreted as proportions and 0/1 data are generated.

Author

Neal Thomas

See Also

emaxsim, RandEmax

Examples

if (FALSE) {
##  example changes the random number seed

doselev<-c(0,5,25,50,100)
n<-c(78,81,81,81,77)

### population parameters for simulation
e0<-2.465375 
ed50<-67.481113 

dtarget<-100
diftarget<-9.032497
emax<-solveEmax(diftarget,dtarget,log(ed50),1,e0)

sdy<-7.967897
pop<-c(log(ed50),emax,e0)  
  
meanlev<-emaxfun(doselev,pop)  

###FixedMean is specialized constructor function for emaxsim
genp<-FixedMean(n,doselev,meanlev,sdy,pop)  

### binary example
n<-rep(500,5)
doselev<-c(0,5,25,50,1000)
dose<-rep(doselev,n)

e0<- qlogis(0.2)
ed50<-20
diftarget<-qlogis(0.6)-qlogis(0.2)
lambda<-2
dtarget<-100
emax<-solveEmax(diftarget,dtarget,log(ed50),lambda,e0)

pop<-c(log(ed50),lambda,emax,e0)
meanlev<-plogis(emaxfun(doselev,pop))

genp<-FixedMean(n,doselev,meanlev,sdy,pop,binary=TRUE)  

tapply(genp$genFun(genp$genP)$y,dose,mean)
meanlev

}
# \dontshow{
##  example changes the random number seed

doselev<-c(0,5,25,50,100)
n<-c(78,81,81,81,77)

### population parameters for simulation
e0<-2.465375 
ed50<-67.481113 

dtarget<-100
diftarget<-9.032497
emax<-solveEmax(diftarget,dtarget,log(ed50),1,e0)

sdy<-7.967897
pop<-c(log(ed50),emax,e0)  
  
meanlev<-emaxfun(doselev,pop)  

###FixedMean is specialized constructor function for emaxsim
gen.parm<-FixedMean(n,doselev,meanlev,sdy,pop)  

# }