next.MinED {MinEDfind} | R Documentation |
Determine the dose for the next cohort of new patients for single-agent trials that aim to find a minimum effective dose (MinED)
Description
Determine the dose for the next cohort of new patients for single-agent trials that aim to find a MinED
Usage
next.MinED(n, y, z, d, phi_t, phi_e, eps_t, eps_e, ct = 0.95, N1 = 18)
Arguments
n |
a vector of number of patients treated at each dose level |
y |
a vector of number of patients experiencing the toxicity at each dose level (with the same length as candidate doses) |
z |
a vector of number of patients showing response at each dose level (with the same length as candidate doses) |
d |
the starting dose level |
phi_t |
the target DLT rate |
phi_e |
the target response rate |
eps_t |
a small value such that (phi_t - eps_t, phi_t + eps_t) is an indifference interval of phi_t. The default value is eps_t = 0.1 * phi_t |
eps_e |
a small value such that (phi_e - eps_e, phi_e + eps_e) is an indifference interval of phi_e. The default value is eps_e = 0.1 * phi_e |
ct |
the cutoff used to eliminate the dose for too toxicity. The default value is ct = 0.95 |
N1 |
number of trials in the stage 1. The default value is N1 = 18 |
Value
next.MinED() returns recommended dose level for the next cohort as a list ($nextdose
)
Author(s)
Chia-Wei Hsu, Fang Wang, Rongji Mu, Haitao Pan, Guoying Xu
References
Rongji Mu, Guoying Xu, Haitao Pan (2020). A nonparametric two-stage Bayesian adaptive design for minimum effective dose (MinED)-based dosing-finding trials, (under review)
Examples
n = c(3, 6, 0, 0, 0)
y = c(0, 1, 0, 0, 0)
z = c(0, 1, 0, 0, 0)
d = 2
phi_t = 0.3
phi_e = 0.3
eps_t = 0.1 * phi_t
eps_e = 0.1 * phi_e
next.dose <- next.MinED(n = n, y = y, z = z, d = d, phi_t = phi_t,
phi_e = phi_e, eps_t = eps_t, eps_e = eps_e)
print(next.dose)