plot.MinED: Plot the simulation results for nonparametric two-stage...

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

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")

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