plot.MinED {MinEDfind}

Plot the simulation results for nonparametric two-stage Bayesian adaptive designs

Description

Plot the objects returned by other functions, including (1) operating characteristics of the design, including selection percentage and the number of patients treated at each dose; (2) the estimates of toxicity and response probability for each dose in the admissable set and corresponding 95% credible interval

Usage

## S3 method for class 'MinED'
plot(x, name, ...)

Arguments

x

the object returned by other functions

name

the name in the object to be plotted

...

ignored arguments

Value

plot.MinED() returns a figure

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

## select the MinED based on the trial data
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, ct = 0.95)
plot.MinED(select.dose)

## get the operating characteristics for nonparametric two-stage Bayesian adaptive designs
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)

plot.MinED(oc, "Sel%")
plot.MinED(oc, "#Pts.treated")
plot.MinED(oc, "#Pts.response.to.tox")
plot.MinED(oc, "#Pts.response.to.eff")