BrextractB: Extract a simulation from the output of emaxsimB

In clinDR: Simulation and Analysis Tools for Clinical Dose Response Modeling

Extract a simulation from the output of emaxsimB

Description

Extract a simulated data set from the output of emaxsimB. Data are re-created using the stored random number seed.

Usage

## S3 method for class 'emaxsimB'
x[i, ...]

Arguments

x	Output object from emaxsimB
i	Simulation replication to extract
...	Parameters passed to other functions (none currently)

Details

Re-creates the ith simulated data set for subsequent analyses. Also returns all analyses done for the ith data set in emaxsimB

Value

A list is returned with class(emaxsimBobj) containing:

y	Response vector
dose	Doses corresponding to y
pop	Population parameters; type of parameter depends on constructor function generating study data.
popSD	Vector containing the population SD used to generate continuous data. NULL for binary data.
binary	When TRUE, binary data modeled on the logit scale
modType	modType=3, 4, for the hyperbolic and sigmoidal Emax models.
predpop	Population means for each dose group
dm	Vector containing dose group means
dsd	Vector containing dose group SDs
fitpred	Posterior means of the dose groups means
sepred	SE (posterior SD) corresponding to the estmates in fitpred
sedif	SE (posterior SD) for the differences with placebo
bfit	Bayesian fitted model of class fitEmaxB.
prior, mcmc	See fitEmax for documentation.
pVal, selContrast	P-value and contrast selected from MCP-MOD test
idmax	Index of default dose group for comparison to placebo

Note

Extraction from a simulation object requires re-creation of the simulated data set. If the extracted object is to be used more than once, it is more efficient to save the extracted object than reuse [].

Author(s)

Neal Thomas

See Also

emaxsimB, print.emaxsimBobj, plot.emaxsimBobj

Examples

## Not run: 

save.seed<-.Random.seed
set.seed(12357)

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

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

dtarget<-100
diftarget<-2.464592
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<-FixedMean(n,doselev,meanlev,sdy)  

prior<-emaxPrior.control(epmu=0,epsca=30,difTargetmu=0,
		difTargetsca=30,dTarget=100,p50=50,sigmalow=0.1,
		sigmaup=30,parmDF=5)
mcmc<-mcmc.control(chains=1,warmup=500,iter=5000,seed=53453,propInit=0.15,adapt_delta = 0.95)

D1 <- emaxsimB(nsim,gen, prior, modType=3,mcmc=mcmc,check=FALSE)

out<-D1[2]


.Random.seed<-save.seed

## End(Not run)

clinDR documentation built on Aug. 9, 2023, 9:08 a.m.