Description Usage Arguments Details Value Note References See Also Examples

View source: R/get.boundary.kb.R

Generates the optimal dose escalation and de-escalation boundaries for conducting a single-agent trial with the KEYBOARD design.

1 2 3 4 5 6 7 8 9 10 11 | ```
get.boundary.kb(
target,
ncohort,
cohortsize,
marginL = 0.05,
marginR = 0.05,
cutoff.eli = 0.95,
n.earlystop = 100,
extrasafe = FALSE,
offset = 0.05
)
``` |

`target` |
the target dose-limiting toxicity (DLT) rate |

`ncohort` |
the total number of cohorts |

`cohortsize` |
the cohort size |

`marginL` |
the difference between the target and the left bound of the
"target key" (proper dosing interval) to be defined. |

`marginR` |
the difference between the target and the right bound of the
"target key" (proper dosing interval) to be defined. |

`cutoff.eli` |
the cutoff to eliminate an overly toxic dose and all
higher doses for safety. |

`n.earlystop` |
the early stopping parameter. If the number of patients treated at
the current dose reaches |

`extrasafe` |
set |

`offset` |
a small positive number (between 0 and 0.5) to control how strict
the stopping rule is when |

The KEYBOARD design relies on the posterior distribution of the toxicity probability to guide dosage. To make the decision of dose escalation and de-escalation, given the observed data at the current dose, we identify the interval that has the highest posterior probability, which we refer to as the "strongest key". This key represents where the true dose-limiting toxicity (DLT) rate of the current dose is most likely located. If the strongest key is located on the left side of the "target key", we escalate the dose (because it means that the observed data suggests that the current dose is most likely to represent under-dosing); if the strongest key is located on the right side of the target key, we de-escalate the dose (because the data suggests that the current dose represents overdosing); and if the strongest key is the target key, we retain the current dose (because the observed data supports that the current dose is most likely to be in the proper dosing interval). Graphically, the strongest key is the one with the largest area under the posterior distribution curve of the DLT rate of the current dose.

An attractive feature of the KEYBOARD design is that its dose escalation and de-escalation rule can be tabulated before the onset of the trial. Thus, when conducting the trial, no calculation or model fitting is needed, and we only need to count the number of DLTs observed at the current dose and make the decision of dose escalation and de-escalation based on the pre-tabulated decision rules.

Given all observed data, the KEYBOARD design uses a statistical technique called isotonic regression to obtain an efficient statistical estimate of the maximum tolerated dose (MTD) by utilizing the fact that toxicity presumably increases with the dose.

For patient safety, we apply the following Bayesian overdose control rule
after each cohort:
if at least 3 patients have been treated at the given dose and
the observed data indicate that the probability of the toxicity rate of
the current dose being above the target toxicity rate is more
than 95%, we eliminate this and any higher dose from the trial to prevent
exposing future patients to these overly toxic doses. The probability
threshold can be specified with `cutoff.eli`

. When a dose is
eliminated, the design recommends the next lower dose for treating the next
patient. If the lowest dose is overly toxic, the trial terminates early and
no dose is selected as the MTD.

The function returns a matrix, which includes the dose escalation and de-escalation boundaries, as well as the elimination boundary.

In most clinical applications, the target DLT rate is often a rough guess, but finding a dose level with a DLT rate reasonably close to the target rate (which ideally would be the MTD) is what interests the investigator.

Yan F, Mandrekar SJ, Yuan Y. KEYBOARD: A Novel Bayesian Toxicity Probability
Interval Design for Phase I Clinical Trials.
*Clinical Cancer Research*. 2017; 23:3994-4003.
http://clincancerres.aacrjournals.org/content/23/15/3994.full-text.pdf

Other single-agent functions:
`get.oc.kb()`

,
`select.mtd.kb()`

1 2 3 4 | ```
### Single-agent trial ###
bound <- get.boundary.kb(target=0.3, ncohort=10, cohortsize=3)
print(bound)
``` |

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