View source: R/functions_multitrial_binary.R
utility23_binary | R Documentation |
The utility function calculates the expected utility of our drug development program and is given as gains minus costs and depends on the parameters and the expected probability of a successful program.
The utility is in further step maximized by the optimal_multitrial_binary()
function.
utility23_binary(
n2,
RRgo,
w,
p0,
p11,
p12,
in1,
in2,
alpha,
beta,
c2,
c3,
c02,
c03,
b1,
b2,
b3
)
n2 |
total sample size for phase II; must be even number |
RRgo |
threshold value for the go/no-go decision rule |
w |
weight for mixture prior distribution |
p0 |
assumed true rate of control group |
p11 |
assumed true rate of treatment group |
p12 |
assumed true rate of treatment group |
in1 |
amount of information for |
in2 |
amount of information for |
alpha |
significance level |
beta |
|
c2 |
variable per-patient cost for phase II |
c3 |
variable per-patient cost for phase III |
c02 |
fixed cost for phase II |
c03 |
fixed cost for phase III |
b1 |
expected gain for effect size category |
b2 |
expected gain for effect size category |
b3 |
expected gain for effect size category |
The output of the function utility23_binary()
is the expected utility of the program depending on whether two or three phase III trials are performed.
utility23_binary(n2 = 50, RRgo = 0.8, w = 0.3,
alpha = 0.05, beta = 0.1,
p0 = 0.6, p11 = 0.3, p12 = 0.5,
in1 = 300, in2 = 600,
c2 = 0.75, c3 = 1, c02 = 100, c03 = 150,
b1 = 1000, b2 = 2000, b3 = 3000)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.