get.boundary(target, ncohort, cohortsize, n.earlystop = 100,
p.saf = "default", p.tox = "default", cutoff.eli = 0.95,
extrasafe = FALSE, offset = 0.05, print = TRUE)n.earlystop, stop the trial and select the MTD based on the observed data. The default value n.earlystop=100 essentially turns off the type op.saf=0.6 x target.p.tox=1.4 x target.(cutoff.eli=0.95) for general use.extrasafe=TRUE to impose a more strict stopping rule for extra safetyextrasafe=TRUE. A larger value leads to a more strict stopping rule. The default value offset=0.05 generally works well.get.boundary() returns the optimal dose escalation and deescalation boundaries for running the trial. The dose elimination boundary is also returned for preventing the continuous exposure of patients to overly toxic doses.get.boundary() also outputs the elimination boundary, which is used to avoid treating patients at overly toxic doses based on the following Bayesian safety rule:
if $pr(p_j>\phi | m_j,n_j)>0.95$ and $n_j \geq 3$, dose levels j and higher are eliminated from the trial,
where $p_j$ is the toxicity probability of dose level j, $\phi$ is the target toxicity rate, and $m_j$ and $n_j$ are the number of toxicities and patients treated at dose level j. The trial is terminated if the lowest dose is eliminated.
The BOIN design has two built-in stopping rules: (1) stop the trial if the lowest dose is eliminated due to toxicity, and no dose should be selected as the MTD; and (2) stop the trial and select the MTD if the number of patients treated at the current dose reaches n.earlystop. The first stopping rule is a safety rule to protect patients from the case in which all doses are overly toxic. The rationale for the second stopping rule is that when there is a large number (i.e., n.earlystop) of patients assigned to a dose, it means that the dose-finding algorithm has approximately converged. Thus, we can stop the trial early and select the MTD to save the sample size and reduce the trial duration.
For some applications, investigators may prefer a more strict safety stopping rule than rule (1) for extra safety when the lowest dose is overly toxic. This can be achieved by setting extrasafe=TRUE, which imposes the following more strict safety stopping rule: stop the trial if (i) the number of patients treated at the lowest dose >=3, and (ii) Pr(toxicity rate of the lowest dose > target | data) > cutoff.eli-offset. As a tradeoff, the strong stopping rule will decrease the MTD selection percentage when the lowest dose actually is the MTD.
Paper:
## Consider a phase I trial aiming to find the MTD with a target toxicity rate of 0.3
## the maximum sample size is 30 patients in cohort size of 3
get.boundary(target=0.3, ncohort=10, cohortsize=3)Run the code above in your browser using DataLab