get.OC.MinED {MinEDfind}

Generate operating characteristics for finding the minimum effective dose (MinED)

Description

Obtain the operating characteristics of the nonparametric two-stage Bayesian adaptive design for minimum effective dose (MinED)-based dosing-finding trials

Usage

get.OC.MinED(ttox, teff, phi_t, phi_e, ct = 0.95, eps_t, eps_e, d0 = 1,
             cohortsize = 3, ncohort1, ncohort2, ntrial = 100, extrasafe = TRUE,
             cutoff.eli = 0.95, n.earlystop = 12)

Arguments

ttox

a vector containing the true toxicity rates of the investigational dose levels

teff

a vector containing the true response rates of the investigational dose levels

phi_t

the target DLT rate

phi_e

the target response rate

ct

the cutoff used to eliminate the dose for too toxicity. The default value is ct = 0.95

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

d0

the starting dose level. The default value is d0 = 1

cohortsize

the cohort size

ncohort1

the number of cohort used in stage I

ncohort2

the number of cohort used in stage II

ntrial

the number of simulated trial

extrasafe

extrasafe set extrasafe = TRUE to impose a more stringent stopping rule

cutoff.eli

the cutoff to eliminate an overly toxic dose for safety. The default value is cutoff.eli = 0.95

n.earlystop

the early stopping parameter. The default value is n.earlystop = 12

Value

get.oc.MinED() returns the operating characteristics of nonparametric two-stage Bayesian adaptive design as a matrix object, including: (1) true DLT rate at each dose level, (2) true efficacy rate at each dose level, (3) selection percentage at each dose level, (4) the average number of patients treated at each dose level, (5) the average number of patients responded to toxicity at each dose level, (6) the average number of patients responded to efficacy at each dose level

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

ttox = c(0.05, 0.15, 0.3, 0.45, 0.6)
teff = c(0.05, 0.15, 0.3, 0.45, 0.6)
phi_t = 0.3
phi_e = 0.3
eps_t = 0.1 * phi_t
eps_e = 0.1 * phi_e

oc = get.OC.MinED(ttox = ttox, teff = teff, phi_t = phi_t, phi_e = phi_e,
                  eps_t = eps_t, eps_e = eps_e, cohortsize = 3, ncohort1 = 6,
                  ncohort2 = 14, ntrial = 100)
print(oc)