next.MinED: Determine the dose for the next cohort of new patients for...

In MinEDfind: A Bayesian Design for Minimum Effective Dosing-Finding Trial

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)

MinEDfind documentation built on July 1, 2020, 10:02 p.m.