Package 'blrm'

Dose Escalation Design in Phase I Oncology Trial using Bayesian Logistic Regression Modeling

Description

Provides dose escalation design in Phase I oncology trial using Bayesian Logistic Regression Modeling given prior and observed cohorts

Usage

blrm_mono_ss(prior, data, output_excel=FALSE, output_pdf=FALSE)
  blrm_mono_ms(prior, data, output_excel=FALSE, output_pdf=FALSE)
  blrm_combo_ss(prior, data, output_excel=FALSE, output_pdf=FALSE)
  blrm_combo_ms(prior, data, output_excel=FALSE, output_pdf=FALSE)

Arguments

prior	mean, standard deviation, and correlation of parameters
data	parameters, including random number generating seeds, number of simulation samples, burn-in period, drug name, dose unit, provisional doses, reference dose, tested doses, number of patients, number of dose-limiting toxicities, category bounds, category names, escalation with overdose criterion
output_excel	output_excel = FALSE by default; if output_excel = TRUE, then the simulation output will be provided in excel file
output_pdf	output_pdf = FALSE by default; if output_pdf = TRUE, then visualization of simulation output, including interval probabilities by dose and posterior distribution of dose-limiting toxicity rate, will be provided in pdf file

Details

Model-based dose-escalation design is more flexible than traditional “3 + 3" design. Bayesian logistic regression model is a two-parameter statistical model to quantify the relationship between dose-limiting toxicity (DLT) rate and drug dose.

$\log(\frac{\pi_d}{1-\pi_d}) = \log(\alpha) + \beta\log(\frac{d}{d^*})$

, where $\alpha > 0$, $\beta > 0$, $\pi_d$ is the probability of toxicity at dose d, and $d^*$ is the reference dose.

For more details, see Neuenschwander, et al. (2008).

Value

prob_posterior	interval probabilities by dose
para_posterior	posterior estimates of parameters
pi_posterior	posterior estimates of dose-limiting rates
next_dose	recommended next dose
current_summary	summary of simulation outputs
cohort_all	accumulated summary of each observed cohort

Author(s)

Furong Sun [email protected]

References

Neuenschwander, B., Branson, M. and Gsponer, T. (2008). “Critical aspects of the Bayesian approach to phase I cancer trials”, Statistics in Medicine, 27(13), 2420-2439. doi: 10.1002/sim.3230.

Examples

## mono version 
  # prior for log(alpha) and log(beta)
  mean <- c(-3.068, 0.564)
  se <- c(2.706, 0.728)
  corr <- -0.917
  prior <- list(mean=mean, se=se, corr=corr)
  
  # parameters
  seeds <- 1:2
  nsamples <- 10000
  burn_in <- 0.2
  drug_name <- "DRUG-X"
  dose_unit <- "mg"
  prov_dose <- c(360, 480, 720, 1080, 1440)
  ref_dose <- 720
  category_bound <- c(0.16, 0.33)
  category_name <- c("under-dosing", "targeted-toxicity", "over-dosing")
  ewoc <- 0.25
  
  # observed cohorts
  dose <- 480
  n_pat <- 3
  dlt <- 0
  
  # combine prior, parameters, and observed cohorts to a list
  data <- list(seeds=seeds, nsamples=nsamples, burn_in=burn_in, drug_name=drug_name,
               dose_unit=dose_unit, prov_dose=prov_dose, ref_dose=ref_dose,
               dose=dose, n_pat=n_pat, dlt=dlt, category_bound=category_bound,
               category_name=category_name, ewoc=ewoc)
  
  # ready to go!
  trial <- blrm_mono_ss(prior=prior, data=data, output_excel=FALSE, output_pdf=FALSE)
  prob_posterior <- trial$prob_posterior
  pi_posterior <- trial$pi_posterior
  
  # visualization
  ## interval probabilities by dose
  cols <- c("green", "red")[((prob_posterior[3,]) > ewoc)+1] # red: stop; green: pass
  yrange <- function(max.y){
      yrange.final <- ifelse(rep((1.1*max.y <= 1), 2), c(0, 1.1*max.y), c(0, max.y))
  }
  layout(matrix(c(1,2,3), 3, 1, byrow=TRUE))
  ## e.g., (0.33, 1]
  barplot(prob_posterior[3,], 
          xlab=paste0("dose", "(", dose_unit, ")"), ylab="probability",
          ylim=yrange(max(prob_posterior[3,])), names.arg=paste(prov_dose), 
          col=cols,
          main=paste0(category_name[3], ": (", category_bound[2], ", 1]"), 
          cex.main=1.3, font.main=4)
  if(max(prob_posterior[3,]) >= ewoc){
     abline(h=ewoc, lty=2, col="red")
     text(x=0.4, y=1.15*ewoc, labels=paste0("EWOC=", ewoc), 
          col="red", cex=1.2, font.main=4)
  }else{
     text(x=0.8, y=max(prob_posterior[3,]), labels=paste0("EWOC=", ewoc), 
          col="red", cex=1.2, font.main=4)
  }
  ## e.g., (0.16, 0.33]
  barplot(prob_posterior[2,], 
          xlab=paste0("dose", "(", dose_unit, ")"), ylab="probability",
          ylim=yrange(max(prob_posterior[2,])), names.arg=paste(prov_dose), 
          col="green",
          main=paste0(category_name[2], ": (", category_bound[1], ", ", category_bound[2], "]"), 
          cex.main=1.3, font.main=4)
  ## e.g., (0, 0.16]
  barplot(prob_posterior[1,], 
          xlab=paste0("dose", "(", dose_unit, ")"), ylab="probability",
          ylim=yrange(max(prob_posterior[1,])), names.arg=paste(prov_dose), 
          col="green", main=paste0(category_name[1], ": (0, ", category_bound[1], "]"), 
          cex.main=1.3, font.main=4)
  ## add a main title to the three barplots together
  mtext("Interval Probabilities by Dose", side=3, outer=TRUE, line=-2,
         at=par("usr")[1]+0.035*diff(par("usr")[1:2]), cex=1.2, font=2)
     
  ## posterior distribution of DLT rate
  par(mfrow=c(1,1))
  plot(prov_dose, pi_posterior[4,], type='p', pch=20, 
       xlab=paste0("dose", "(", dose_unit, ")"), ylab="DLT rate", 
       xlim=range(prov_dose), ylim=c(0, max(pi_posterior)), 
       main="Posterior Distribution of DLT Rate", bty="n")
  arrows(prov_dose, pi_posterior[3,], prov_dose, pi_posterior[5,], 
         code=3, angle=90, length=0.1, lwd=1.5, col=1)
  if(max(pi_posterior[5,]) >= category_bound[2]){
     abline(h=category_bound, lty=2, col=c(rgb(0,1,0,alpha=0.8), rgb(1,0,0,alpha=0.8)))
     legend("topleft", c(paste(category_bound), "median", "95 percent credible interval"),
            lty=c(2,2,NA,1), lwd=c(1,1,NA,1.5), pch=c(NA,NA,20,NA),
            col=c(rgb(0,1,0,alpha=0.8), rgb(1,0,0,alpha=0.8), 1, 1), bty="n")
  }else if((max(pi_posterior[5,]) >= category_bound[1]) && 
            (max(pi_posterior[5,]) < category_bound[2])){
     abline(h=category_bound[1], lty=2, col=rgb(0,1,0,alpha=0.8))
     legend("topleft", c(paste(category_bound[1]), "median", "95 percent credible interval"),
            lty=c(2,NA,1), lwd=c(1,NA,1.5), pch=c(NA,20,NA), 
            col=c(rgb(0,1,0,alpha=0.8), 1, 1), bty="n")
  }else{
        legend("topleft", c("median", "95 percent credible interval"), 
                lty=c(NA,1), lwd=c(NA,1.5), pch=c(20,NA), col=c(1,1), bty="n")
  }
     
  

  ## combo version
  # prior
  ## drug 1
  mean1 <- c(-1.0989, -0.1674)
  se1 <- c(1.2770, 0.5713)
  corr1 <- 0.5224
  prior1 <- list(mean=mean1, se=se1, corr=corr1)
  ## drug 2
  mean2 <- c(-2.9444, 0)
  se2 <- c(2, 1)
  corr2 <- 0
  prior2 <- list(mean=mean2, se=se2, corr=corr2)
  ## interaction between 2 drugs
  prior3 <- list(mean=0, se=1.121)
  ## combine three sets of priors
  prior <- list(prior1, prior2, prior3)
    
  # parameters
  seeds <- 1:2
  nsamples <- 10000
  burn_in <- 0.5
    
  ## drug 1
  drug1_name <- "DRUG-X"
  dose1_unit <- "mg"
  ref_dose1 <- 15
  prov_dose1 <- c(1, 2.5, 5, 10)
    
  ## drug 2
  drug2_name <- "DRUG-Y"
  dose2_unit <- "mg"
  ref_dose2 <- 350
  prov_dose2 <- c(200, 250, 300, 350)
    
  dose1 <- 1     # tested doses for drug 1
  dose2 <- 200   # tested doses for drug 2
  n_pat <- 3     # number of patients at each observed cohort
  dlt <- 0       # number of DLTs at each observed cohort
    
  category_bound <- c(0.16, 0.33)
  category_name <- c("under-dosing", "targeted-toxicity", "over-dosing")
  ewoc <- 0.25
    
  # combine to a list
  data <- list(seeds=seeds, nsamples=nsamples, burn_in=burn_in,
               drug1_name=drug1_name, dose1_unit=dose1_unit, 
               ref_dose1=ref_dose1, prov_dose1=prov_dose1,
               drug2_name=drug2_name, dose2_unit=dose2_unit, 
               ref_dose2=ref_dose2, prov_dose2=prov_dose2,
               dose1=dose1, dose2=dose2, n_pat=n_pat, dlt=dlt,
               category_bound=category_bound, category_name=category_name, 
               ewoc=ewoc)
    
  # ready to go!
           trial <- blrm_combo_ss(prior=prior, data=data, output_excel=FALSE, output_pdf=FALSE)
  prob_posterior <- trial$prob_posterior
    pi_posterior <- trial$pi_posterior
       next_dose <- trial$next_dose
    
  # visualization
  ## Interval Probabilities by Dose: `(0.33, 1]' is the target
  ### data manipulation
  prov_dose <- expand.grid(prov_dose1, prov_dose2)
  prob_posterior_3 <- cbind(prov_dose, prob_posterior[3,])
  names(prob_posterior_3) <- c("drug1", "drug2", "probability")
  prob_posterior_3 <- transform(prob_posterior_3, 
                                level=ifelse(probability > ewoc, 1, 0))
  for(i in 1:nrow(prob_posterior_3)){
      if(as.numeric(rownames(prob_posterior_3[i,])) == as.numeric(next_dose$index)){
         prob_posterior_3[i,4] <- 2
      }
  }
  # convert data.frame from ``long" to ``wide"
  prob_posterior_3_wide <- reshape2::dcast(prob_posterior_3[,-3], 
                                     drug2 ~ drug1, value.var="level")
  prob_posterior_3_wide <- prob_posterior_3_wide[,-1]
  rownames(prob_posterior_3_wide) <- paste(prov_dose2)
  colnames(prob_posterior_3_wide) <- paste(prov_dose1)
  cols <- matrix(NA, nrow=length(prov_dose2), ncol=length(prov_dose1))
  for(i in 1:nrow(prob_posterior_3_wide)){
      for(j in 1:ncol(prob_posterior_3_wide)){
          if(prob_posterior_3_wide[i,j]==0){
             cols[i,j] <- "green"
          }else if(prob_posterior_3_wide[i,j]==1){
             cols[i,j] <- "red"
          }else{
             cols[i,j] <- "blue"
          }
      }
  }
  ### generate the plot
  plot(NA, NA, type='n', xaxt='n', yaxt='n', cex.lab=1.5, 
       cex.main=2,
       xlim=range(1:length(prov_dose1)), 
       ylim=range(1:length(prov_dose2)),  
       xlab=paste0(drug1_name, "(", dose1_unit, ")"), 
       ylab=paste0(drug2_name, "(", dose2_unit, ")"), 
       main="Dose combo Categorization")
  abline(h=1:length(prov_dose2), v=1:length(prov_dose1), 
         lty=2, lwd=1, col="gray")
  axis(1, at=1:length(prov_dose1), labels=paste(prov_dose1))        
  axis(2, at=1:length(prov_dose2), labels=paste(prov_dose2), las=2)
  # add dose combos falling within different categories
  for(i in 1:length(prov_dose2)){
      for(j in 1:length(prov_dose1)){
          points(j, i, pch=19, col=cols[i,j], cex=4)
      }
  }
  legend("topright", c(" <= EWOC", " > EWOC", "Recommended Next Dose"), 
         cex=1.1, pch=rep(19, 2), col=c("green", "red", "blue"), 
         pt.cex=2, xpd=TRUE, horiz=TRUE, inset=c(0, -0.045), bty='n')
    
  ## Posterior Distribution of DLT Rate
  par(mfrow=c(1,1))
  labels <- apply(prov_dose, 1, paste, collapse=",")
  plot(1:nrow(prov_dose), pi_posterior[4,], type="p", pch=20, 
       xlab="drug combo", xaxt='n', cex.lab=1.5,
       ylab="DLT rate", ylim=c(0, max(pi_posterior)), 
       main="Posterior Distribution of DLT Rate", cex.main=2.0, bty='n')
  axis(1, at=1:nrow(prov_dose), labels=FALSE)
  text(x=1:nrow(prov_dose), par("usr")[3]-0.03, labels=labels, 
       srt=90, pos=1, xpd=TRUE, cex=0.5)
  arrows(1:nrow(prov_dose), pi_posterior[3,], 
         1:nrow(prov_dose), pi_posterior[5,], 
         code=3, angle=90, length=0.1, lwd=1.5, col=1) 
  if(max(pi_posterior[5,]) >= category_bound[2]){
     abline(h=category_bound, lty=2, 
            col=c(rgb(0,1,0,alpha=0.8), rgb(1,0,0,alpha=0.8))) 
     legend("top", 
            c(paste(category_bound), "median", "95 percent credible interval"), 
            lty=c(2,2,NA,1), lwd=c(1,1,NA,1.5), pch=c(NA,NA,20,NA), 
            col=c(rgb(0,1,0,alpha=0.8), rgb(1,0,0,alpha=0.8), 1, 1), 
            xpd=TRUE, horiz=TRUE, inset=c(0, -0.035), bty='n')
  }else if((max(pi_posterior[5,]) >= category_bound[1]) && 
           (max(pi_posterior[5,]) < category_bound[2])){
      abline(h=category_bound[1], lty=2, col=rgb(0,1,0,alpha=0.8)) 
      legend("top", c(paste(category_bound[1]), "median", "95 percent credible interval"), 
             lty=c(2,NA,1), lwd=c(1,NA,1.5), pch=c(NA,20,NA), 
             col=c(rgb(0,1,0,alpha=0.8), 1, 1), bty='n',
             xpd=TRUE, horiz=TRUE, inset=c(0, -0.035))
  }else{
      legend("top", c("median", "95 percent credible interval"), 
             lty=c(NA, 1), lwd=c(NA, 1.5), pch=c(20, NA), col=c(1, 1),
             xpd=TRUE, horiz=TRUE, inset=c(0, -0.035), bty='n')
  }
  # end of visualization

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