dtat1000: Precomputed neutrophil-guided chemotherapy dose titration for...
In DTAT: Dose Titration Algorithm Tuning

dtat1000

R Documentation

Precomputed neutrophil-guided chemotherapy dose titration for 1000 simulated subjects.

Description

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.

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.)

References

Norris DC. Dose Titration Algorithm Tuning (DTAT) should supersede ‘the’ Maximum Tolerated Dose (MTD) in oncology dose-finding trials. F1000Research. 2017;6:112. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.12688/f1000research.10624.3")}. https://f1000research.com/articles/6-112/v3
Norris DC. Costing ‘the’ MTD. bioRxiv. August 2017:150821. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1101/150821")}. https://www.biorxiv.org/content/10.1101/150821v3

Examples


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))

DTAT documentation built on March 31, 2023, 10:26 p.m.