CFO (version 2.1.0)

CFO2d.selectmtd: Select the maximum tolerated dose (MTD) for the real drug combination trials

Description

Select the maximum tolerated dose (MTD) when the real drug combination trials is completed

Usage

CFO2d.selectmtd(target, npts, ntox, 
       prior.para = list(alp.prior = target, bet.prior = 1 - target), 
       cutoff.eli = 0.95, early.stop = 0.95, verbose = TRUE)

Value

CFO2d.selectmtd() returns

• target: the target DLT rate.

• MTD: the selected MTD. MTD = (99, 99) indicates that all tested doses are overly toxic.

• p_est: the isotonic estimate of the DLT probablity at each dose and associated \(95\%\) credible interval. p_est = NA if all tested doses are overly toxic.

• p_est_CI: the credible interval for the isotonic estimate. p_est_CI = NA if all tested doses are overly toxic.

Arguments

target

the target DLT rate.

npts

a matrix containing the number of patients treated at each dose level.

ntox

a matrix containing the number of patients who experienced DLT at each 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.

verbose

set verbose = TRUE to return more details of the results.

Author

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

Details

CFO2d.selectmtd() selects the MTD based on isotonic estimates of toxicity probabilities. CFO2d.selectmtd() selects as the MTD dose \(j^*\), for which the isotonic estimate of the DLT rate is closest to the target. If there are ties, we select from the ties the highest dose level when the estimate of the DLT rate is smaller than the target, or the lowest dose level when the estimate of the DLT rate is greater than the target. The isotonic estimates are obtained by the pooled-adjacent-violators algorithm (PAVA).

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.
Wang W, Jin H, Zhang Y, Yin G (2023). Two-dimensional calibration-free odds (2dCFO) design for phase I drug-combination trials. Frontiers in Oncology, 13, 1294258.
Bril G, Dykstra R, Pillers C, Robertson T (1984). Algorithm AS 206: Isotonic regression in two independent variables. Journal of the Royal Statistical Society. Series C (Applied Statistics), 33(3), 352–357.

Examples

ntox <- matrix(c(0, 0, 2, 0, 0,
                0, 2, 7, 0, 0,
                0, 2, 0, 0, 0), 
              nrow = 3, ncol = 5, byrow = TRUE)

npts <- matrix(c(3,  0, 12, 0, 0,
                3, 12, 24, 0, 0,
                3,  3,  0, 0, 0), 
              nrow = 3, ncol = 5, byrow = TRUE)
selmtd <- CFO2d.selectmtd(target=0.3, npts=npts, ntox=ntox)
summary(selmtd)
plot(selmtd)