utility_multiple_tte {drugdevelopR} | R Documentation |
Utility function for multiple endpoints in a time-to-event-setting
Description
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 a further step maximized by the optimal_multiple_tte()
function.
Note, that for calculating the utility of the program, two different benefit triples are necessary:
one triple for the case that the more important endpoint overall survival (OS) shows a significant positive treatment effect
one triple when only the endpoint progression-free survival (PFS) shows a significant positive treatment effect
Usage
utility_multiple_tte(
n2,
HRgo,
alpha,
beta,
hr1,
hr2,
id1,
id2,
c2,
c02,
c3,
c03,
K,
N,
S,
steps1,
stepm1,
stepl1,
b11,
b21,
b31,
b12,
b22,
b32,
fixed,
rho,
rsamp
)
Arguments
n2 |
total sample size for phase II; must be even number |
HRgo |
threshold value for the go/no-go decision rule; |
alpha |
significance level |
beta |
|
hr1 |
assumed true treatment effect on HR scale for endpoint OS |
hr2 |
assumed true treatment effect on HR scale for endpoint PFS |
id1 |
amount of information for |
id2 |
amount of information for |
c2 |
variable per-patient cost for phase II |
c02 |
fixed cost for phase II |
c3 |
variable per-patient cost for phase III |
c03 |
fixed cost for phase III |
K |
constraint on the costs of the program, default: Inf, e.g. no constraint |
N |
constraint on the total expected sample size of the program, default: Inf, e.g. no constraint |
S |
constraint on the expected probability of a successful program, default: -Inf, e.g. no constraint |
steps1 |
lower boundary for effect size category |
stepm1 |
lower boundary for effect size category |
stepl1 |
lower boundary for effect size category |
b11 |
expected gain for effect size category |
b21 |
expected gain for effect size category |
b31 |
expected gain for effect size category |
b12 |
expected gain for effect size category |
b22 |
expected gain for effect size category |
b32 |
expected gain for effect size category |
fixed |
choose if true treatment effects are fixed or random, if TRUE |
rho |
correlation between the two endpoints |
rsamp |
sample data set for Monte Carlo integration |
Value
The output of the function utility_multiple_tte()
is the expected utility of the program.