Generate dosing decisions (E, S, D or DU) of the mTPI2 design for user-specified number of participants.
get_decision_mtpi2(pT,
EI,
npts)get_decision_mtpi2() 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.
a numeric value that specifies the target DLT rate (\(p_T\)).
a vector that specifies the equivalence interval (EI).
the number of participants within which dosing decisions are generated.
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 mTPI2 design divides the probability of DLT into equal-width intervals: underdosing, target dosing, and overdosing. Utilizing a Bayesian model, mTPI-2 updates the posterior probability estimates of DLTs. If the interval which maximizes the posterior probability is among the underdosing intervals, the decision is to escalate to the next higher dose; if the interval which maximizes the posterior probability is the target dosing interval, the decision is to stay at the current dose; if the interval which maximizes the posterior probability is among the overdosing intervals, the decision is to to de-escalate to the next lower dose.
Also, the mTPI2 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.
Guo, W., Liu, S., & Yin, G. (2017). A more efficient Bayesian model for oncology dose-finding trials with toxicity probability interval. Clinical Trials, 14(1), 16-26.
get_decision_mtpi2(pT = 0.25,
EI = c(0.2,0.3),
npts = 12)
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