bpchkMonoEmax: Bayes posterior predictive test for Emax (monotone) model fit

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

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. checkMonoEmax is deprecated. See bpchkMonoEmax.

Usage

bpchkMonoEmax(x, trend='positive', protSel=1)

Arguments

x	Output object of class 'fitEmaxB'.
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.
protSel	The test is applied to the data from a single protocol. The protocol can be selected if the model was fit to data from more than one protocol. The protSel must match a protocol value input to fitEmaxB or it numerical index value, 1,2,...

Details

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.

When fitEmaxB is applied to first-stage fitted model output with a non-diagonal variance-covariance matrix, the predictive draws are selected from a multivariate model with means computed from the MCMC-generated parameters and input asymptotic variance-covariance matrix vcest. If the fitted model was applied to binary data, the GOF statistic is computed based on the logit rather than observed dose group sample proportion scale. This differs from the setting with patient-level data input to fitEmaxB.

Value

Returns a scalar Bayesian predictive p-value.

Author(s)

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>

Wu, J., Banerjee, A., Jin, B. Menon, M. S., Martin, S. and Heatherington, A. (2017). Clinical dose response for a broad set of biological products: A model-based meta-analysis. Statistical Methods in Medical Research. <doi:10.1177/0962280216684528>

See Also

plot.plotB, plotD, plot.fitEmax

Examples

## Not run: 

data("metaData")
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(fitout, trend='negative')
      

## End(Not run)

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