An R6 class encapsulating Cumulative-Cohort Designs
An R6 class encapsulating Cumulative-Cohort Designs
precautionary::Cpe
-> Ccd
new()
Ccd$new(escalate, deescalate, eliminate, cohort_max, enroll_max)
escalate
Escalation boundary
deescalate
Deescalation boundary
eliminate
Elimination boundary
cohort_max
Upper bound on dose-wise enrollment
enroll_max
Upper bound on total enrollment
Create a new Ccd
object.
A Ccd
object.
# TODO
applied()
Ccd$applied(x, o, last_dose, max_dose, ...)
x
A dose-wise vector of toxicity counts
o
A dose-wise vector of non-toxicity counts
last_dose
The most recently given dose, as required to implement cumulative-cohort-based escalation decisions.
max_dose
An upper limit on future dose levels
...
Unused by Ccd
; included for superclass method compatibility
Return dose recommendation for given tox/no-tox tallies.
An object with components:
$stop
- logical value indicating whether stop is indicated
$mtd
- integer value, the recommended dose
$max_dose
- integer value, a dose not to be exceeded henceforth.
clone()
The objects of this class are cloneable with this method.
Ccd$clone(deep = FALSE)
deep
Whether to make a deep clone.
TODO: Explain the hierarchy of model classes, including connections
with the executable specifications set forth in exec/prolog/ccd.pl
.
Ivanova A, Flournoy N, Chung Y. Cumulative cohort design for dose-finding. Journal of Statistical Planning and Inference. 2007;137(7):2316-2327. 10.1016/j.jspi.2006.07.009
Liu S, Yuan Y. Bayesian optimal interval designs for phase I clinical trials. J R Stat Soc C. 2015;64(3):507-523. 10.1111/rssc.12089
# NOT RUN {
## ------------------------------------------------
## Method `Ccd$new`
## ------------------------------------------------
# TODO
# }
Run the code above in your browser using DataLab