Escalation and De-escalation Interval Boundaries

Compute the two boundaries that define the BOIN decision interval. A dose is escalated when the observed DLT rate falls at or below lambda_e and de-escalated when it reaches lambda_d.

Usage

boin_lambda(target, p_saf = NULL, p_tox = NULL)

Arguments

target

Numeric scalar. Target DLT probability, for example 0.30.

p_saf

Numeric scalar. Highest DLT probability deemed subtherapeutic, so that dose escalation should be undertaken. Defaults to 0.6 * target.

p_tox

Numeric scalar. Lowest DLT probability deemed overly toxic, so that dose de-escalation is required. Defaults to 1.4 * target.

Value

A list with components lambda_e and lambda_d.

Details

The boundaries minimize the probability of incorrect dose assignment under a three-point hypothesis on the DLT probability at the current dose. Values of p_saf and p_tox close to target should be avoided, because the sample sizes of phase I trials cannot distinguish the target rate from rates close to it.

References

Liu S. and Yuan, Y. (2015). Bayesian Optimal Interval Designs for Phase I Clinical Trials. Journal of the Royal Statistical Society: Series C, 64, 507-523.

Examples

boin_lambda(target = 0.30)
#> $lambda_e
#> [1] 0.2364907
#> 
#> $lambda_d
#> [1] 0.3585195
#> 

boin_lambda(target = 0.25, p_saf = 0.12, p_tox = 0.40)
#> $lambda_e
#> [1] 0.1788381
#> 
#> $lambda_d
#> [1] 0.3219281
#>