oc {earlygating} | R Documentation |
Single Arm Operating Characteristics
Description
Function for calculating the operating characteristics of the single arm Bayesian designs in setting 1 and 2 for early gating.
Usage
oc(
N_e,
delta,
delta_power,
confidence,
e_a = 0.5,
e_b = 0.5,
h_a = 0.5,
h_b = 0.5,
RR_h = NULL,
N_h = NULL,
hist_RR_c = NULL,
trues = seq(0, 1, 0.01),
adapt = 1,
plot = T,
legend = T,
legend.pos = "topleft"
)
Arguments
N_e |
Sample Size in the experimental group. Can be either a single value or a vector. |
delta |
Required superiority to make a "GO" decision. Corresponds to |
delta_power |
Superiority, at which decision power will be evaluated.
Corresponds to |
confidence |
Required confidence to make "GO" decision. Corresponds to |
e_a |
Alpha parameter of Beta Prior Distribution for the experimental response rate.
Corresponds to |
e_b |
Beta parameter of Beta Prior Distribution for the experimental response rate.
Corresponds to |
h_a |
Alpha parameter of Beta Prior Distribution for the historical control response rate.
Corresponds to |
h_b |
Beta parameter of Beta Prior Distribution for the historical control response rate.
Corresponds to |
RR_h |
Historical control response rate. Corresponds to |
N_h |
Historical control sample size. Corresponds to |
hist_RR_c |
Point estimate of historical control repsonse rate. Corresponds to |
trues |
Sequence of true control response rates and experimental response rates, at which the Probability to Go will be computed. Default is seq(0,1,0.01) to ensure continuous plots and accurate results. |
adapt |
Level of adapting of experimental control rate to account for patient selection bias
from phase II to phase III. Corresponds to |
plot |
Plots yes or no. Default is TRUE. |
legend |
Logical; whether or not to include legend in plot. Default is TRUE. |
legend.pos |
Position of legend. Default is "topleft". |
Value
A matrix containing the decision power and decision alpha with respect to the true control response rate.
Examples
# Setting 1
oc(
N_e = 50, delta = 0.08, delta_power = 0.13,
confidence = 0.6, hist_RR_c = 0.5
)
# Setting 2
oc(
N_e = 50, delta = 0.08, delta_power = 0.13,
confidence = 0.6, RR_h = 0.5, N_h = 50
)