Determine the dose for the next cohort of new patients for single-agent trials that aim to find a MTD defined by the Equivalent Score (ET) in Quasi-CRM design (Yuan et al. 2007) and Robust-Quasi-CRM design (Pan et al. 2014) using the gBOIN design (Mu et al. 2017)
next_QuasiBOIN(target, n, y, d, p.saf = 0.6 * target, p.tox = 1.4 * target,
cutoff.eli = 0.95, extrasafe = FALSE, n.earlystop = 100)the target DLT rate
the number of patients enrolled at each dose level
the toxicity score at each dose level
the current dose level
the lower bound. The default value is p.saf = 0.6 * target
the upper bound. The default value is p.tox = 1.4 * target
the cutoff to eliminate an overly toxic dose for safety. The default value is cutoff.eli = 0.95
extrasafe set extrasafe = TRUE to impose a more stringent stopping . The default value is extrasafe = FALSE
the early stopping parameter. The default value is n.earlystop = 100
next_QuasiBOIN() returns recommended dose level for the next cohort as a numeric value under quasi-binary measure
Yuan, Z., R. Chappell, and H. Bailey. "The continual reassessment method for multiple toxicity grades: a Bayesian quasi-likelihood approach." Biometrics 63, no. 1 (2007): 173-179.
Pan, Haitao, Cailin Zhu, Feng Zhang, Ying Yuan, Shemin Zhang, Wenhong Zhang, Chanjuan Li, Ling Wang, and Jielai Xia. "The continual reassessment method for multiple toxicity grades: a Bayesian model selection approach." PloS one 9, no. 5 (2014): e98147.
Mu, Rongji, Ying Yuan, Jin Xu, Sumithra J. Mandrekar, and Jun Yin. "gBOIN: a unified model-assisted phase I trial design accounting for toxicity grades, and binary or continuous end points." Journal of the Royal Statistical Society. Series C: Applied Statistics 68, no. 2 (2019): 289-308.
# NOT RUN {
target <- 0.47 / 1.5
n <- c(3, 3, 6, 3, 3, 0)
y <- c(0, 0, 1.333333, 0, 1, 0)
d <- 5
next_QuasiBOIN(target = target, n = n, y = y, d = d)
# }
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