Package: ewoc 0.3.0
Marcio A. Diniz
ewoc: Escalation with Overdose Control
An implementation of a variety of escalation with overdose control designs introduced by Babb, Rogatko and Zacks (1998) <doi:10.1002/(SICI)1097-0258(19980530)17:10%3C1103::AID-SIM793%3E3.0.CO;2-9>. It calculates the next dose as a clinical trial proceeds and performs simulations to obtain operating characteristics.
Authors:
ewoc_0.3.0.tar.gz
ewoc_0.3.0.tar.gz(r-4.5-noble)ewoc_0.3.0.tar.gz(r-4.4-noble)
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ewoc.pdf |ewoc.html✨
ewoc/json (API)
NEWS
| # Install 'ewoc' in R: |
| install.packages('ewoc', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/dnzmarcio/ewoc/issues
Last updated 5 years agofrom:38b0441625. Checks:1 OK, 2 NOTE. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 17 2025 |
| R-4.5-linux | NOTE | Mar 17 2025 |
| R-4.4-linux | NOTE | Mar 17 2025 |
Exports:dlt_curve_d1classicaldlt_curve_d1extendeddlt_curve_d1phdlt_rateewoc_d1classicalewoc_d1extendedewoc_d1phewoc_simulationinv_standard_doselogitmtd_biasmtd_msemtd_rho_d1extendedopcoptimal_mtdoptimal_toxicitypdlt_d1classicalpdlt_d1extendedpdlt_d1phresponse_d1classicalresponse_d1extendedresponse_d1phstandard_dosestop_rulestop_rule_d1classicalstop_rule_d1extendedstop_rule_d1ph
Dependencies:clicodacodetoolscolorspacedigestdoParalleldoRNGfansifarverforeachFormulaggplot2gluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrjagsrlangrngtoolsscalestibbleutf8vctrsviridisLitewithr
Citation
To cite package ‘ewoc’ in publications use:
Diniz MA (2020). ewoc: Escalation with Overdose Control. R package version 0.3.0, https://CRAN.R-project.org/package=ewoc.
ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see ‘help("citation")’.
Corresponding BibTeX entry:
@Manual{,
title = {ewoc: Escalation with Overdose Control},
author = {Marcio A. Diniz},
year = {2020},
note = {R package version 0.3.0},
url = {https://CRAN.R-project.org/package=ewoc},
}
Readme and manuals
EWOC
Escalation With Overdose Control is a dose escalation design for phase I clinical trials such that the probability of overdose is controlled explicitly.
It was first introduced by Babb et al. (1998) and several modifications have been studied along of the years. This R-package has three available designs: the classical EWOC introduced by Babb et al. (1998), the proportional hazards model in discussed Tighioaurt (2014), and the extended parametrization presented by Tighioaurt et al (2017).
Installation
Before installing the R-package EWOC, you may need to install Just Another Gibbs Sampler.
The R-package EWOC can be installed from GitHub with:
# install.packages("devtools")
devtools::install_github("dnzmarcio/ewoc")
Example
A new dose using the classical EWOC can be calculated:
library(ewoc)
DLT <- 0
dose <- 30
test <- ewoc_d1classic(DLT ~ dose, type = 'discrete',
theta = 0.33, alpha = 0.25,
min_dose = 0, max_dose = 100,
dose_set = seq(0, 100, 20),
rho_prior = matrix(1, ncol = 2, nrow = 1),
mtd_prior = matrix(1, ncol = 2, nrow = 1),
rounding = "nearest")
summary(test)
#> Conditions
#> Minimum Dose Maximum Dose Theta Alpha Number of patients
#> 1 0 100 0.33 0.25 1
#>
#> Next Dose
#> Estimate 95% HPD
#> 1 40 (12.87 ; 98.77)
#>
#> P(DLT| next dose)
#> Estimate 95% HPD
#> 1 0.3 (0.07 ; 0.7)
In addition, simulations also can be performed to evaluate a design:
library(ewoc)
DLT <- 0
dose <- 20
step_zero <- ewoc_d1classical(DLT ~ dose, type = 'discrete',
theta = 0.33, alpha = 0.25,
min_dose = 20, max_dose = 100,
dose_set = seq(0, 100, 20),
rho_prior = matrix(1, ncol = 2, nrow = 1),
mtd_prior = matrix(1, ncol = 2, nrow = 1),
rounding = "nearest")
response_sim <- response_d1classical(rho = 0.05, mtd = 60, theta = 0.33,
min_dose = 20, max_dose = 100)
sim <- ewoc_simulation(step_zero = step_zero,
n_sim = 1, sample_size = 30,
alpha_strategy = "conditional",
response_sim = response_sim,
ncores = 1)
pdlt <- pdlt_d1classical(rho = 0.05, mtd = 60, theta = 0.33,
min_dose = 20, max_dose = 100)
results <- opc(sim_list = list(sim), pdlt_list = list(pdlt),
mtd_list = list(60), toxicity_margin = 0.05, mtd_margin = 6)
References
Babb, J., Rogatko, A., & Zacks, S. (1998). Cancer phase I clinical trials: efficient dose escalation with overdose control. Statistics in medicine, 17(10), 1103-1120.
Tighiouart, M., Liu, Y., & Rogatko, A. (2014). Escalation with overdose control using time to toxicity for cancer phase I clinical trials. PloS one, 9(3), e93070.
Tighiouart, M., Cook-Wiens, G., & Rogatko, A. (2018). A Bayesian adaptive design for cancer phase I trials using a flexible range of doses. Journal of biopharmaceutical statistics, 28(3), 562-574.
Diniz, M. A., Tighiouart, M., & Rogatko, A. (2019). Comparison between continuous and discrete doses for model based designs in cancer dose finding. PloS one, 14(1).
