FIND (version 0.1.1)

get_decision_boin: Dosing decision for the BOIN design

Description

Generate dosing decisions (E, S, D or DU) of the BOIN design for user-specified number of participants.

Usage

get_decision_boin(pT,
                         EI,
                         npts)

Value

get_decision_boin() returns:

(1) a dataframe containing the decisions (E, S, D or DU) for each combination of y and n ($tab),

(2) a list ($setup) containing user input parameters, such as target, EI, npts, etc.

Arguments

pT

a numeric value that specifies the target DLT rate (\(p_T\)).

EI

a vector that specifies the equivalence interval (EI).

npts

the number of participants within which dosing decisions are generated.

Details

Denote the current dose \(d\). Let \(n_d\) and \(y_d\) represent the number of participants treated at dose \(d\) and the number of participants experienced DLT, respectively. Let \(p_d\) be the toxicity probability at dose \(d\). Also, denote \(\frac{y_d}{n_d}\) the observed toxicity rate at the current dose.

The BOIN design uses the following decision rules: if \(\frac{y_d}{n_d}\) is lower than or equal to the escalation boundary, the decision is to escalate to the next higher dose; if \(\frac{y_d}{n_d}\) is higher than the de-escalation boundary, the decision is to the next lower dose; otherwise, the decision is to stay at the current dose.

Also, the BOIN design includes a dose exclusion rule. Let \(p_T\) represents the target DLT rate. If \(Pr(p_d > p_T | y_d , n_d ) > 0.95\), dose \(d\) and those higher than \(d\) are removed from the trial since they are deemed excessively toxic.

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

get_decision_boin(pT = 0.25,
                  EI = c(0.15,0.35),
                  npts = 12)