Cefamandole: PK after IV with six subjects in JAGS

After last week’s post on Theoph I noticed the MEMSS library contained more PK data sets. The Cefamandole data is an IV data set with six subjects. It is somewhat challenging, since there seem to be several elimination rates.

Data

Data are displayed below. Notice that the lines seem somewhat curved, there is clearly more going on that just simple elimination.

Models

The first model fits concentration as function of two eliminations. The problem here is that the eliminations need to be ordered. All faster eliminations should have one prior, all slower another. After much fiddling around I found that I cannot force the ordering at subject level with priors or limits. Hence I chose a different way out. The model gets an extra component, it will predict 0 if the parameters are incorrectly ordered. This indeed gave the desired result in terms of model behavior.

The model itself though, was less to my linking. It goes way too low at the later times. Upon consideration, since the model assumes homoscedastic error it does not matter to the likelihoood if the fit at the lower end is not so good. After all, an error of 1 is nothing compared to errors of 10 or 20 at the upper end of the scale. In addition it can be argued that the error is not homoscedastic. If it were homoscedastic, the underlying data would show way more variation at the lower end of the scale. It would need confirmation from the measuring team, but for now I will assume proportional error.

Model 2: Proportional error

The proportional error will be implemented by using a log normal distribution for concentration. This was pretty simple to code. The log of the expected concentration is used in combination with dlnorm(). The only issue remaining is that expectation 0 for incorrectly ordered parameters has to be shifted a bit, log(0) is a bit of an issue.
This model looks pretty decent. There are still some things to do, similar to last week, translate the parameters c1star and c2star into the parameters which can be interpreted from PK perspective.

Model outputs

Model 1

Inference for Bugs model at “C:/Users/Kees/AppData/Local/Temp/RtmpSe5nAn/modele207c187661.txt”, fit using jags,
 4 chains, each with 10000 iterations (first 5000 discarded), n.thin = 10
 n.sims = 2000 iterations saved
          mu.vect sd.vect    2.5%     25%     50%     75%    97.5%  Rhat n.eff
C[1]       91.473  24.508  51.690  76.444  88.708 103.600  144.987 1.016   160
C[2]      324.947 132.978 147.584 253.433 304.913 366.175  626.711 1.007   460
Ke[1]       0.020   0.002   0.016   0.018   0.019   0.021    0.024 1.045    76
Ke[2]       0.141   0.192   0.081   0.116   0.132   0.147    0.202 1.072   180
c1star[1]  58.783   8.381  43.892  53.222  58.514  63.406   76.601 1.047    64
c1star[2]  59.841  18.962  32.887  47.487  56.324  67.606  108.087 1.048    72
c1star[3]  92.337  10.569  70.647  85.720  92.805  99.469  112.338 1.041    74
c1star[4]  89.554  12.223  67.975  81.086  88.831  96.932  116.699 1.027   110
c1star[5] 146.419  21.504 106.395 132.153 145.199 159.149  193.558 1.032    84
c1star[6] 121.228  14.872  87.712 112.706 122.379 130.819  148.106 1.092    42
c2star[1] 426.241 107.777 278.572 355.121 406.845 471.583  703.121 1.038    92
c2star[2] 201.118  35.861 149.478 179.179 197.081 217.084  278.248 1.033   830
c2star[3] 489.294 218.162 268.493 349.208 419.178 537.325 1125.062 1.088    42
c2star[4] 449.402  68.164 340.627 402.410 440.666 487.652  603.535 1.038    75
c2star[5] 402.518  43.391 335.760 373.724 397.703 423.902  498.253 1.017   380
c2star[6] 140.893  46.737  79.548 114.343 133.240 157.967  237.080 1.024   650
ke1[1]      0.020   0.002   0.016   0.018   0.020   0.021    0.025 1.042    74
ke1[2]      0.021   0.004   0.016   0.019   0.020   0.023    0.032 1.060    57
ke1[3]      0.018   0.002   0.014   0.017   0.018   0.019    0.022 1.031    99
ke1[4]      0.020   0.002   0.017   0.019   0.020   0.021    0.025 1.023   120
ke1[5]      0.020   0.002   0.016   0.019   0.020   0.021    0.024 1.036    74
ke1[6]      0.019   0.002   0.015   0.018   0.019   0.020    0.022 1.071    46
ke2[1]      0.167   0.025   0.128   0.150   0.164   0.181    0.225 1.050    64
ke2[2]      0.101   0.026   0.067   0.085   0.096   0.111    0.164 1.050    91
ke2[3]      0.181   0.042   0.122   0.151   0.173   0.200    0.283 1.076    43
ke2[4]      0.143   0.019   0.110   0.130   0.141   0.155    0.181 1.040    74
ke2[5]      0.112   0.017   0.086   0.100   0.110   0.121    0.151 1.027   130
ke2[6]      0.118   0.038   0.062   0.093   0.114   0.137    0.205 1.051    61
deviance  464.708   7.996 451.264 458.919 464.029 469.608  482.206 1.006   550

For each parameter, n.eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor (at convergence, Rhat=1).

DIC info (using the rule, pD = var(deviance)/2)
pD = 31.8 and DIC = 496.6
DIC is an estimate of expected predictive error (lower deviance is better).

Model 2

Code used

library(MEMSS)
library(ggplot2)
library(R2jags)
png(‘Cefamandole.png’)
qplot(y=conc,x=Time,col=Subject,data=Cefamandole) +
    scale_y_log10(‘cefamandole (mcg/ml)’)+
    geom_line()+
    theme(legend.position=’bottom’)
dev.off()

library(R2jags)
datain <-  list(
    time=Cefamandole$Time,
    conc=Cefamandole$conc,
    n=nrow(Cefamandole),
    subject =c(1:nlevels(Cefamandole$Subject))[Cefamandole$Subject],
    nsubject = nlevels(Cefamandole$Subject)
)

model1 <- function() {
  tau <- 1/pow(sigma,2)
  sigma ~ dunif(0,100)
  for (i in 1:n) {
    pred[i] <- (ke1[subject[i]]<ke2[subject[i]])*
        (c1star[subject[i]]*exp(-ke1[subject[i]]*time[i])+
          c2star[subject[i]]*exp(-ke2[subject[i]]*time[i]))
    conc[i] ~ dnorm(pred[i],tau)
  }
  
  for(i in 1:nsubject) {
    ke1[i] ~ dlnorm(ke[1],kemtau[1])
    ke2[i] ~ dlnorm(ke[2],kemtau[2]) 
    c1star[i] ~ dlnorm(cm[1],ctau[1])
    c2star[i] ~ dlnorm(cm[2],ctau[2])
  }
  for (i in 1:2) {
    kem[i] ~ dunif(-10,10) 
    kemtau[i] <- 1/pow(kesd[i],2) 
    kesd[i] ~ dunif(0,10)
    Ke[i] <- exp(ke[i])
    cm[i] ~ dunif(-10,20)
    ctau[i] <- 1/pow(csd[i],2)
    csd[i] ~ dunif(0,100)
    C[i] <- exp(cm[i])    
  }
  ke <- sort(kem)
} 

parameters <- c(‘Ke’,’ke1′,’ke2′,’c1star’,’c2star’,’C’)
inits <- function() {
  list(ke1 = rnorm(6,0.13,.03),
      ke2=  rnorm(6,0.0180,.005), 
      kem=  log(c(rnorm(1,0.13,.03),rnorm(1,0.0180,.005))),
      kesd =runif(2,.1,.4),
      cm = log(rnorm(2,25,5)),
      c1star=rnorm(6,25,3),
      c2star=rnorm(6,25,3)
  )}
jagsfit1 <- jags(datain, model=model1, 
    inits=inits,
    parameters=parameters,progress.bar=”gui”,
    n.iter=10000,
    n.chains=4,n.thin=10)
jagsfit1
#plot(jagsfit1)
#traceplot(jagsfit1)

Time <- seq(0,max(Cefamandole$Time))
la1 <- sapply(1:nrow(jagsfit1$BUGSoutput$sims.matrix),
    function(i) {
      C1 <- jagsfit1$BUGSoutput$sims.matrix[i,’C[1]’]
      C2 <- jagsfit1$BUGSoutput$sims.matrix[i,’C[2]’]
      k1 <- jagsfit1$BUGSoutput$sims.matrix[i,’Ke[1]’]
      k2 <- jagsfit1$BUGSoutput$sims.matrix[i,’Ke[2]’]
      y=C1*exp(-k1*Time)+C2*exp(-k2*Time)
    })
res1 <- data.frame(
    Time=Time,
    Conc=apply(la1,1,mean),
    C05=apply(la1,1,FUN=function(x) quantile(x,0.05)),
    C95=apply(la1,1,FUN=function(x) quantile(x,0.95)))
png(‘cef1.png’)
ggplot(res1, aes(x=Time)) +
    geom_line(aes(y=Conc))+
    scale_y_log10(‘cefamandole (mcg/ml)’)+
    geom_line(aes(y=conc,x=Time,col=Subject),alpha=1,data=Cefamandole)+
    geom_ribbon(aes(ymin=C05, ymax=C95),alpha=.2)+
    theme(legend.position=’bottom’)
dev.off()
#########
#
# model 2: proportional error
#
#########
model2 <- function() {
  tau <- 1/pow(sigma,2)
  sigma ~ dunif(0,100)
  for (i in 1:n) {
    pred[i] <- log(
        (ke1[subject[i]]<ke2[subject[i]])*
            (c1star[subject[i]]*exp(-ke1[subject[i]]*time[i])+
              c2star[subject[i]]*exp(-ke2[subject[i]]*time[i]))
            +.001*(ke1[subject[i]]>ke2[subject[i]]))
    conc[i] ~ dlnorm(pred[i],tau)
  }
  
  
  for(i in 1:nsubject) {
    ke1[i] ~ dlnorm(ke[1],kemtau[1])
    ke2[i] ~ dlnorm(ke[2],kemtau[2]) 
    c1star[i] ~ dlnorm(cm[1],ctau[1])
    c2star[i] ~ dlnorm(cm[2],ctau[2])
  }
  for (i in 1:2) {
    kem[i] ~ dunif(-10,10) 
    kemtau[i] <- 1/pow(kesd[i],2) 
    kesd[i] ~ dunif(0,10)
    Ke[i] <- exp(ke[i])
    cm[i] ~ dunif(-10,20)
    ctau[i] <- 1/pow(csd[i],2)
    csd[i] ~ dunif(0,100)
    C[i] <- exp(cm[i])    
  }
  ke <- sort(kem)
  
} 

parameters <- c(‘Ke’,’ke1′,’ke2′,’c1star’,’c2star’,’C’)
inits <- function() {
  list(ke1 = rnorm(6,0.13,.03),
      ke2=  rnorm(6,0.0180,.005), 
      kem=  log(c(rnorm(1,0.13,.03),rnorm(1,0.0180,.005))),
      kesd =runif(2,.1,.4),
      cm = log(rnorm(2,25,5)),
      c1star=rnorm(6,25,3),
      c2star=rnorm(6,25,3)
  )}
jagsfit2 <- jags(datain, model=model2, 
    inits=inits,
    parameters=parameters,progress.bar=”gui”,
    n.iter=10000,
    n.chains=4,n.thin=10)
jagsfit2
la2 <- sapply(1:nrow(jagsfit2$BUGSoutput$sims.matrix),
    function(i) {
      C1 <- jagsfit2$BUGSoutput$sims.matrix[i,’C[1]’]
      C2 <- jagsfit2$BUGSoutput$sims.matrix[i,’C[2]’]
      k1 <- jagsfit2$BUGSoutput$sims.matrix[i,’Ke[1]’]
      k2 <- jagsfit2$BUGSoutput$sims.matrix[i,’Ke[2]’]
      y=C1*exp(-k1*Time)+C2*exp(-k2*Time)
    })


res2 <- data.frame(
    Time=Time,
    Conc=apply(la2,1,mean),
    C05=apply(la2,1,FUN=function(x) quantile(x,0.05)),
    C95=apply(la2,1,FUN=function(x) quantile(x,0.95)))

png(‘cef2.png’)
ggplot(res2, aes(x=Time)) +
    geom_line(aes(y=Conc))+
    scale_y_log10(‘cefamandole (mcg/ml)’)+
    geom_line(aes(y=conc,x=Time,col=Subject),alpha=1,data=Cefamandole)+
    geom_ribbon(aes(ymin=C05, ymax=C95),alpha=.2)+
    theme(legend.position=’bottom’)
dev.off()