aCFO.next: Determination of the dose level for next cohort in the accumulative calibration-free odds (aCFO) design for phase I trials

Description

In the aCFO design for phase I trials, the function is used to determine the dose movement based on the toxicity outcomes of the enrolled cohorts.

Usage

aCFO.next(target, ays, ans, currdose, 
       prior.para = list(alp.prior = target, bet.prior = 1 - target),
       cutoff.eli = 0.95, early.stop = 0.95)

Value

The aCFO.next() function returns a list object comprising the following elements:

target: the target DLT rate.
ays: the cumulative counts of DLTs observed at all dose levels.
ans: the cumulative counts of patients treated at all dose levels.
decision: the decision in the aCFO design, where left, stay, and right represent the movement directions, and stop indicates stopping the experiment.
currdose: the current dose level.
nextdose: the recommended dose level for the next cohort. nextdose = 99 indicates that the trial is terminated due to early stopping.
overtox: the situation regarding which position experiences over-toxicity. The dose level indicated by overtox and all the dose levels above experience over-toxicity. overtox = NA signifies that the occurrence of over-toxicity did not happen.
toxprob: the expected toxicity probability, \(Pr(p_k > \phi | x_k, m_k)\), at all dose levels, where \(p_k\), \(x_k\), and \(m_k\) is the dose-limiting toxicity (DLT) rate, the numbers of observed DLTs, and the numbers of patients at dose level \(k\). NA indicates that there are no patients at the corresponding dose level.

Arguments

target: the target DLT rate.
ays: the cumulative numbers of DLTs observed in patients for all dose levels.
ans: the cumulative numbers of patients for all dose levels.
currdose: the current dose level.
prior.para: the prior parameters for a beta distribution, where set as list(alp.prior = target, bet.prior = 1 - target) by default, alp.prior and bet.prior represent the parameters of the prior distribution for the true DLT rate at any dose level. This prior distribution is specified as Beta(alpha.prior, beta.prior).
cutoff.eli: the cutoff to eliminate overly toxic doses for safety. We recommend the default value of cutoff.eli = 0.95 for general use.
early.stop: the threshold value for early stopping. The default value early.stop = 0.95 generally works well.

Author

Jialu Fang, Ninghao Zhang, Wenliang Wang, and Guosheng Yin

Details

The aCFO design is an extension of the CFO design. It integrates dose information from all positions (ranging from the lowest to the highest dose levels) into the decision-making process of the trial. Before assigning the dose level for a new cohort, aCFO compares the evidence from the current dose level with all doses to its left and right. In contrast, the original CFO design makes dose allocation by examining one dose level above and one below the current dose level. Consequently, the aCFO design enhances the utilization of information while maintaining the characteristics of the CFO design (model-free and calibration-free). Additionally, the aCFO design preserves the same early stopping and dose elimination criteria as the CFO design.

References

Jin H, Yin G (2022). CFO: Calibration-free odds design for phase I/II clinical trials. Statistical Methods in Medical Research, 31(6), 1051-1066.
Fang J, Yin G (2024). Fractional accumulative calibration‐free odds (f‐aCFO) design for delayed toxicity in phase I clinical trials. Statistics in Medicine, 43(17), 3210-3226.

Examples

Run this code

## determine the dose level for the next cohort of new patients
ays <- c(0, 0, 1, 0, 0, 0, 0); ans <- c(3, 3, 6, 0, 0, 0, 0)
decision <- aCFO.next(target = 0.2, ays = ays, ans = ans, currdose = 3, 
            prior.para = list(alp.prior = 0.2, bet.prior = 0.8))
summary(decision)

ays <- c(3, 0, 0, 0, 0, 0, 0); ans <- c(3, 0, 0, 0, 0, 0, 0)
decision <- aCFO.next(target = 0.2, ays = ays, ans = ans, currdose = 1,
            prior.para = list(alp.prior = 0.2, bet.prior = 0.8))
summary(decision)

ays <- c(0, 0, 0, 0, 0, 0, 3); ans <- c(3, 3, 3, 3, 3, 3, 3)
decision <- aCFO.next(target = 0.2, ays = ays, ans = ans, currdose = 7,
            prior.para = list(alp.prior = 0.2, bet.prior = 0.8))
summary(decision)

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