# Precomputed neutrophil-guided chemotherapy dose titration for 1000 simulated subjects. This dataset is provided to support fast reproduction of a forthcoming pharmacoeconomic paper that includes examination of the empirical distribution of MTDi in N=1000 simulated subjects. data ## Format A data frame showing end-of-cycle state of neutrophil-guided dose titration for 1000 simulated subjects, across 10 cycles of chemotherapy. - **cycle**: Cycle number 1..10 - **id**: Subject identifiers; an ordered factor with levels `id1` < `...` < `id1000` - **Cc**: Central-compartment drug concentration - **Cp**: Peripheral-compartment drug concentration - **Prol**: Progenitor cells in proliferating compartment of Friberg et al. (2002) model - **Tx.1**: Transit compartment 1 - **Tx.2**: Transit compartment 1 - **Tx.3**: Transit compartment 1 - **Circ**: Concentration (cells/mm^3) of circulating neutrophils - **dose**: Dose of 1-hour infusion administered this cycle - **CircMin**: Neutrophil nadir (cells/mm^3) - **tNadir**: Time (days) of neutrophil nadir - **scaled.dose**: Fourth root of dose - **time**: Time (weeks) of dose administration ## Details Running the examples interactively, you can verify the reproducibility of this dataset. (That demo is included in a `donttest` block to spare the CRAN servers.) ## Examples ```r data(dtat1000) # 1. Extract the N final doses, assuming convergence by the tenth course MTD_i <- with(dtat1000, dose[time==27]) MTD_i <- MTD_i[MTD_i < 5000] # Exclude few outliers # 2. Do a kernel density plot library(Hmisc) library(latticeExtra) hist <- histogram(~MTD_i, breaks=c(0,100,200,300,400,600,900,1500,2500,4000,5000) , xlab=expression(MTD[i])) approx <- data.frame(mtd_i=seq(0, 5000, 10)) approx <- upData(approx, gamma = dgamma(mtd_i, shape=1.75, scale=200)) dist <- xyplot(gamma ~ mtd_i, data=approx, type='l', col='black', lwd=2) library(grid) hist + dist grid.text(expression(MTD[i] %~% paste("Gamma(", alpha==1.75, ", ", beta==1/200,")")) , x=unit(0.5,"npc") , y=unit(0.75,"npc") ) ## A very long repro, which a user of this package may well wish to verify ## by running the examples interactively, although it takes many minutes ## to compute. (Enclosed in a dontest block to avoid overburdening CRAN.) # Demonstrate close reproduction of original titration (the titration takes many minutes!) set.seed(2016) library(pomp) Onoue.Friberg(N=1000) # This titration may take an hour to run ... chemo <- titrate(doserange = c(50, 3000), dta=newton.raphson(dose1 = 100, omega = 0.75, slope1 = -2.0, slopeU = -0.2) ) dtat1k <- upData(chemo$course , time = 3*(cycle-1) , labels = c(time="Time") , units = c(time="weeks") , print = FALSE) c10dose1k <- subset(dtat1k, cycle==10)$scaled.dose c10dose1000 <- subset(dtat1000, cycle==10)$scaled.dose stopifnot(0.999 < cor(c10dose1k, c10dose1000)) ``` ## References 1. Norris DC. Dose Titration Algorithm Tuning (DTAT) should supersede the Maximum Tolerated Dose (MTD) in oncology dose-finding trials. **F1000Research**. 2017;6:112. tools:::Rd_expr_doi("10.12688/f1000research.10624.3") . [https://f1000research.com/articles/6-112/v3](https://f1000research.com/articles/6-112/v3) 2. Norris DC. Costing the MTD. **bioRxiv**. August 2017:150821. tools:::Rd_expr_doi("10.1101/150821") . [https://www.biorxiv.org/content/10.1101/150821v3](https://www.biorxiv.org/content/10.1101/150821v3)

dtat1000 dataset