emaxsimB {clinDR} R Documentation

Simulate Emax Bayesian estimation

Description

Simulate dose response data and apply 4- or 3- parameter sigmoidal or hyperbolic Bayesian estimation. The prior distribution is input by the user with default values for some parameters based on the empirical distribution estimated from dose response meta-analyses. For binary response data, the Emax model is fit on the logit scale, and then back-transformed

Usage

emaxsimB(nsim, genObj, prior, modType = 4,
binary = FALSE, seed=12357,
check = FALSE, nproc=parallel::detectCores(),
negEmax = FALSE, ed50contr = NULL,
lambdacontr = NULL, testMods = NULL,
idmax = length(doselev),
mcmc = mcmc.control(),
customCode=NULL, customParms=NULL,
description = "")


Arguments

 nsim Number of simulation replications genObj Object containing inputs and function to create simulated data sets. These objects are created by special constructor functions; the current choices are FixedMean and RandEmax. prior Prior specification through an object of type 'emaxPrior' or 'prior'. See emaxPrior.control and prior.control for details. The 'emaxPrior' specifies the magnitude of the potential effect for a specified dose (typically the highest anticipated dose and/or the dose in a POC study), while the 'prior' specifies the theoretical maximum effect (the emax parameter). The 'prior' specification is deprecated and will be removed. modType When modType=3, a hyperbolic Emax model is fit. When modType=4, a sigmoid Emax model is fit. binary When specified, the Emax model is fit on the logit scale, and then the results are back-transformed to proportions. seed Seed for random number generator used to create data. A separate seed can be passed to rstan through the MCMC object. check When TRUE, a single simulated data set is created and the data and rstan object are returned for convergence checking. The data are in the form needed for developing customCode. Note that customCode is not called when check=TRUE. nproc The number of processors to use in parallel computation of the simulations, which are divided into equal-sized computational blocks. When nproc=1 a single local processor. negEmax When TRUE, the intended effect is assumed to be negative. ed50contr A vector of ED50 values for creating a global null test using the MCP-MOD package DoseFinding based on Emax model-based contrasts. The default is 3 contrasts: the mid-point between pbo and the lowest dose, the mid-point between the 2 highest doses, and the median of the dose levels. When there are <=4 doses including pbo, the median-based contrast is excluded. lambdacontr Hill parameters matched to the ed50contr. The default value is 1 for each contrast model. testMods The model object for a MCP-MOD test created by Mods from package DoseFinding. If specified, the other contrast inputs are ignored. The Mods call should use the unique sorted dose levels. The direction of the trend should be specificed in the call to Mods. The negEmax is stored for use by support functions, but it does not determine the direction of the effect when testMods is specified. The validity of testMods is not checked. idmax Index of the default dose group for comparison to placebo. Most analysis functions allow other dose groups to be specified. The default is the index of the highest dose. mcmc MCMC settings created using mcmc.control customCode An optional user supplied function that computes custom estimates/decision criteria from each simulated data set and its Bayesian model fit. The output are stored in a list, customOut, of length nsim. See the Details section below for a description of the mandatory inputs to the customCode function. customParms Optional parameters that can be passed to customCode. description Optional text describing the simulation setting that is stored with the simulation output.

Details

The Bayesian model fits are implemented in rstan using function fitEmaxB. The function compileStanModels must be executed once to create compiled STAN code before emaxsimB can be used.

Continuous data can be simulated from any dose response curve with homogeneous normally distributed residuals.

Binary data are handled similarly. The models are fit on the logit scale and then back-transformed for estimation of dose response. Reduced linear models are selected based on the corresponding likelihood deviance.

MCP-MOD tests are created from contrasts based on the Emax function using the DoseFinding package. Different ED50 and lambda (Hill) parameters can be specified to form the contrasts. A contrast matrix output from the DoseFinding package can be specified instead, allowing for other contrast choices.

Customized code:

For binary data, the inputs to the function customCode for each simulated data set will be (parms,pVal,dose,y), where parms is the matrix of parameters generated from the posterior distribution with columns in the order given in function emaxfun, pVal is the MCP-MOD p-value, dose and y are the patient-level simulated data. For continuous data, the inputs are (parms,residSD,pVal,dose,y), where residSD are the variance parameters generated from their posterior distribution. The customParms supply other user-inputs such as a target efficacy level. When it is not null, the customCode inputs must be (parms,pVal,dose,y,customParms) or (parms,residSD,pVal,dose,y,customParms).

Value

A list is returned with class(emaxsim) containing:

 description User description of simulation localParm localParm=TRUE when the prior prior distribution is input using emaxPrior. binary Binary response data. modType Type of Emax model fit (3 or 4 parameters) genObj List object with data and function used to generate study data pop Matrix with rows containing population parameters for each simulation. 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. mcmc mcmc input settings prior Input prior distribution. est Matrix with posterior median parameter estimates for each simulation: (led50,lambda,emax,e0,difTarget) or (led50,emax,e0,difTarget). The difTarget are omitted for the deprecated distribution. estlb,estub Array with lower posterior (0.025,0.05,0.1) and upper posterior (0.975,0.95,0.9) percentiles of the model parameters. The array ordering is model parameters, simulation, and percentile. residSD The posterior median of the residual SD for each simulation. pVal The nsim p-values from the global null test. The p-values are 1-sided computed using MCP-Mod. selContrast The index of the test contrast producing the smallest p-value. testMods Object of class Mods from R package DoseFinding that defines the contrasts used in MCP-MOD testing. The functions can be plotted with DoseFinding loaded. gofP Goodness of fit test computed by checkMonoEmax. negEmax User input stored for subsequent reference. predpop Matrix with population means for each dose group mv Matrix with rows containing dose group sample means sdv Matrix with rows containing dose group sample SD msSat Pooled within-dose group sample variance fitpredv Matrix with rows containing dose groups means estimated by the posterior medians of the MCMC generated values. sepredv Matrix with rows containing SE (posterior SD) associated with fitpredv fitdifv Matrix with rows containing dose groups mean differences wih placebo estimated by the posterior medians of the differences of the MCMC generated values. sedifv Matrix with rows containing SE (posterior SD) for the differences with placebo lb,ub Array with lower posterior (0.025,0.05,0.1) and upper posterior (0.975,0.95,0.9) percentiles of differences between dose group means and placebo. The array ordering is dose group minus placebo, simulation, and percentile. divergence The proportion of divergent MCMC iterations from each simulated analysis. rseed Starting random number seed for each simulated data set set that can be assigned to .Random.seed. To reproduce the data, the random number generator must also be changed to RNGkind("L'Ecuyer-CMRG"). idmax Index of default dose group for comparison to placebo (e.g., for plotting Z-statistics). customOut List with customized output. It will be NULL if customCOde is not specified.

Note

The default modType was changed from 3 to 4 for clinDR version >2.0

Neal Thomas

References

Thomas, N., Sweeney, K., and Somayaji, V. (2014). Meta-analysis of clinical dose response in a large drug development portfolio, Statistics in Biopharmaceutical Research, Vol. 6, No.4, 302-317. <doi:10.1080/19466315.2014.924876>

Thomas, N., and Roy, D. (2016). Analysis of clinical dose-response in small-molecule drug development: 2009-2014. Statistics in Biopharmaceutical Research, Vol. 6, No.4, 302-317 <doi:10.1080/19466315.2016.1256229>

print.emaxsimB, summary.emaxsimB, plot.emaxsimB, coef.emaxsimB, sigma.emaxsimB, emaxfun

Examples

## Not run:

### emaxsimB changes the random number seed

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,

### custom code to compute the distribution of the dose yielding
### a target diff with pbo
customCode<-function(parms,residSD,pVal,dose,y,customParms){
target<-customParms
ed50<-exp(parms[,1])
emax<-parms[,2]
td<-ifelse(emax-target>0,ed50*(target/(emax-target)),Inf)
tdest<-median(td)
lb<-quantile(td,0.1)
ub<-quantile(td,0.9)
return(c(td=tdest,lb=lb,ub=ub))
}

D1 <- emaxsimB(nsim,gen, prior, modType=4,seed=12357,mcmc=mcmc,check=FALSE,
customCode=customCode,customParms=1.0)
D1

## End(Not run)


[Package clinDR version 2.3.5 Index]