select.MinED {MinEDfind}

Select the minimum effective dose (MinED) for single agent trials

Description

Select the minimum effective dose (MinED) when the trial is completed

Usage

select.MinED(n, y, z, phi_t, phi_e, eps_t, eps_e, ct = 0.95)

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)

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

Value

select.MinED() returns the selected dose with detailed information as a list, including: (1) selected dose level ($Selected_Dose), (2) target level for efficacy and toxicity rate ($Target_Level), (3) posterior estimate of efficacy and toxicity with its corresponding lower and upper bound etc. ($Info)

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)
phi_t = 0.3
phi_e = 0.3
eps_t = 0.1 * phi_t
eps_e = 0.1 * phi_e
select.dose <- select.MinED(n, y, z, phi_t, phi_e, eps_t, eps_e)
print(select.dose)