clinDR: targetD – R documentation

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targetD

Compute the MLE (and its SE) of the dose achieving a specified target improvement from placebo.

Description

The MLE (se) of the dose required to achieve a targetted improvement from placebo. The fit can be from a 3- or 4- parameter Emax model or output from function emaxalt, or an object of class emaxsimobj. The Emax model is on the logit scale for binary data.

Usage

targetD (fit,
	target,
	modType=4,
	binary=FALSE)

Arguments

`fit`	Output of `nls` `fit` to a 3- or 4-parameter Emax model. The order of the parameters in the fit must be (log(ed50),emax,e0) or (log(ed50),lambda,emax,e0). `fit` can also be a list with the first element the coefficient vector, and the second element the variance-covariance matrix. Alternatively, `fit` may be of class `emaxalt` or `emaxsimobj`, and the target dose is based on the fitted model.
`target`	Targetted change from placebo (positive or negative).
`modType`	Value is 3 or 4 for the 3 or 4-parameter Emax model output from nls with parameters in the order (ed50,emax,e0) or (ed50,lambda,emax,e0). `modType` is ignored if fit is from `emaxalt` or `emaxsimobj`.
`binary`	When `TRUE`, the fit is assumed to be for binary data on the logistic scale. `target` is input as a risk difference, and transformed internally. When the `fit` is of class `emaxalt` or `emaxsimobj`, the `binary` status is taken from the object and `binary` is ignored.

Value

Returns a vector with two elements:

`targetDose`	The MLE of the dose achieving the target.
`seTD`	SE for target.dose

Note

Asymptotic SE computed using the delta method

Author(s)

Neal Thomas

Examples

## Not run: 

		## emaxsim changes the random number seed
    doselev<-c(0,5,25,50,100,250)
    n<-c(78,81,81,81,77,80)
    dose<-rep(doselev,n)

    ### 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(led50=log(ed50),emax=emax,e0=e0)    
    meanresp<-emaxfun(dose,pop)  
    y<-rnorm(sum(n),meanresp,sdy)
    nls.fit<-nls(y ~ e0 + (emax * dose)/(dose + exp(led50)), 
                              start = pop, control = nls.control(
                              maxiter = 100),trace=TRUE,na.action=na.omit)

    targetD(nls.fit,10,modType=3)

    ###
    ### apply targetD to an emaxsim object
    ###
    nsim<-50
    sdy<-25
    gen.parm<-FixedMean(n,doselev,emaxfun(doselev,pop),sdy)  
    D4 <- emaxsim(nsim,gen.parm,modType=4)
    summary(D4,testalph=0.05)

    out<-NULL
    for(i in 1:nsim){
        out<-rbind(out,targetD(D4[i],target=4))
    }

## End(Not run)

clinDR

Simulation and Analysis Tools for Clinical Dose Response Modeling

v2.3.5

GPL (>= 2)

Authors

Neal Thomas [aut, cre] (<https://orcid.org/0000-0002-1915-8487>), Jing Wu [aut], Mike K. Smith [aut]

Initial release

2021-04-11