clinDR (version 2.4.1)

predict.fitEmax: Estimated mean/proportion and confidence intervals derived from the maximum likelihood fit of a 3- or 4- parameter Emax model.

Description

The estimated means from an Emax model is computed along with confidence bounds. The results are computed for a vector of input dose levels. For binary outcomes, the results are computed on the logit scale and then back-transformed.

Usage

# S3 method for fitEmax
predict(object,dosevec,clev=0.9, 
                  int=1,dref=0, xvec=NULL, ...)

Value

A list with estimated dose group means/proportions, lower bound, upper bound, SE, and corresponding values for differences with the reference dose. One value for each dose in dosevec.

Arguments

object

Output of fitEmax with class "fitEmax".

dosevec

Vector of doses to be evaluated.

clev

Confidence level for intervals about the estimated mean/proportion at each dosevec.

int

The index for the protocol (intercept) to use for the predictions

dref

Differences in response between doselev and dref are computed.

xvec

The vector of centered baseline values for the prediction model when xbase was specified in the model fit. Centering must be done using the protocol-specific means consistent with int. See details for the default calculations when xvec is not specified.

...

No additonal parameters will be utilized.

Author

Neal Thomas

Details

Model estimates, standard errors, and confidence bounds are computed with the function SeEmax.

If baseline covariates were included in the fit and xvec is not specified, then the predicted value is the mean of the predictions for all patients in the specified protocol. Note that the protocol must be specified, or the prediction defaults to patients from the first protocol. Note that for binary data, the distinction between the mean of the predicted values and the predicted value as the mean of the covariates can be important.

See Also

nls

Examples

if (FALSE) {
## this example changes the random number seed
doselev<-c(0,5,25,50,100,350)
n<-c(78,81,81,81,77,80)

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

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

sdy<-8.0
pop.parm<-c(log(ed50),emax,e0)    
dose<-rep(doselev,n)
meanlev<-emaxfun(dose,pop.parm)  

y<-rnorm(sum(n),meanlev,sdy)

testout<-fitEmax(y,dose,modType=4)
predout<-predict(testout,dosevec=c(20,80),int=1)
}
# \dontshow{
## this example changes the random number seed
doselev<-c(0,5,25,50,100,350)
n<-c(78,81,81,81,77,80)

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

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

sdy<-8.0
pop.parm<-c(log(ed50),emax,e0)    
dose<-rep(doselev,n)
meanlev<-emaxfun(dose,pop.parm)  

y<-rnorm(sum(n),meanlev,sdy)

testout<-fitEmax(y,dose,modType=4)
predout<-predict(testout,dosevec=c(20,80),int=1)
# }