checkMonoEmax {clinDR} R Documentation

## Bayes posterior predictive test for Emax (monotone) model fit

### Description

Bayes posterior predictive test for an Emax (monotone) model fit comparing the best response from lower doses to the response from the highest dose.

### Usage

checkMonoEmax(y,
dose,
parm,
sigma2,
nvec=rep(1,length(dose)),
xbase=NULL,
modelFun=emaxfun,
trend='positive',
binary= FALSE,logit=binary)


### Arguments

 y Outcomes. Continuous y can be individual data or group means. Binary y can be individual data, group proportions, or 0/1 data with correspondng counts, as is required by fitEmaxB. dose Doses corresponding to outcomes parm Matrix of simultated parameter values (each row is a simulated parameter vector). The parm values must be constructed for use in the model function modFun. The default is a 4-parameter Emax model with parameters (log(ED50),lambda,Emax,E0). For a 3-parameter model, set lambda=1 for each simulated parameter vector. sigma2 Simulated draws from the residual variance (assumed additive, homogeneous). The length of sigma2 must be the same as the number of rows of parm. sigma2 is ignored when binary=TRUE nvec The number of observations contributing to each y. The default is 1 for patient-level data. xbase Optional covariates matching y. nvec must be 1 (patient-level) data. The coeficients for xbase are the final columns of parm. modelFun The mean model function. The first argument is a scalar dose, and the second argument is a matrix of parameter values. The rows of the matrix are random draws of parameter vectors for the model. The default function is the 4-parameter Emax function emaxfun. trend The default is 'positive', so high values for lower doses yield small Bayesian predictive probabilities. Set trend to 'negative' for dose response curves with negative trends. binary If TRUE, the inverse logit transform is applied to the (Emax) function output for comparison to dose group sample proportions, and the predictive data are sampled from a binomial distribution. logit logit is deprecated, use binary

### Details

A sample of parameters from the joint posterior distribution must be supplied (typically produced by an MCMC program). The Bayesian predictive p-value is the posterior probability that a dose group sample mean in a new study with the same sample sizes would yield a higher (or lower for negative trend) difference for one of the lower doses versus the highest dose than was actually obtained from the real sample. There must be at least two non-placebo dose groups (NA returned otherwise). Placebo response is excluded from the comparisons.

The function generates random numbers, so the random number generator/seed must be set before the function is called for exact reproducibility.

### Value

Returns a scalar Bayesian predictive p-value.

### Author(s)

Neal Thomas

plot.plotB, plotD, plot.fitEmax

### Examples

## Not run:

exdat<-metaData[metaData$taid==6 & metaData$poptype==1,]

prior<-emaxPrior.control(epmu=0,epsca=10,difTargetmu=0,difTargetsca=10,dTarget=80.0,
p50=3.75,sigmalow=0.01,sigmaup=20)
mcmc<-mcmc.control(chains=3)

msSat<-sum((exdat$sampsize-1)*(exdat$sd)^2)/(sum(exdat$sampsize)-length(exdat$sampsize))
fitout<-fitEmaxB(exdat$rslt,exdat$dose,prior,modType=4,
count=exdat$sampsize,msSat=msSat,mcmc=mcmc) parms<-coef(fitout)[,1:4] #use first intercept checkMonoEmax(y=exdat$rslt, dose=exdat$dose, parm=parms, sigma2=(sigma(fitout))^2, nvec=exdat$sampsize, trend='negative')

## End(Not run)



[Package clinDR version 2.3.5 Index]