utility_multiarm_normal {drugdevelopR} | R Documentation |
Utility function for multiarm programs with normally distributed outcomes
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 as on the sample size and expected probability of a successful program.
The utility is in further step maximized by the optimal_multiarm_normal()
function.
Usage
utility_multiarm_normal(
n2,
kappa,
alpha,
beta,
Delta1,
Delta2,
strategy,
c2,
c02,
c3,
c03,
K,
N,
S,
steps1,
stepm1,
stepl1,
b1,
b2,
b3
)
Arguments
n2 |
total sample size for phase II; must be even number |
kappa |
threshold value for the go/no-go decision rule |
alpha |
significance level |
beta |
1-beta power for calculation of sample size for phase III |
Delta1 |
assumed true treatment effect for standardized difference in means |
Delta2 |
assumed true treatment effect for standardized difference in means |
strategy |
choose Strategy: 1 ("only best promising"), 2 ("all promising") |
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 "small", default: 0 |
stepm1 |
lower boundary for effect size category "medium" = upper boundary for effect size category "small" default: 0.5 |
stepl1 |
lower boundary for effect size category "large" = upper boundary for effect size category "medium", default: 0.8 |
b1 |
expected gain for effect size category "small" |
b2 |
expected gain for effect size category "medium" |
b3 |
expected gain for effect size category "large" |
Value
The output of the function utility_multiarm_normal()
is the expected utility of the program.
Examples
res <- utility_multiarm_normal(n2 = 50, kappa = 0.8, alpha = 0.05, beta = 0.1,
Delta1 = 0.375, Delta2 = 0.625, strategy = 1,
c2 = 0.75, c3 = 1, c02 = 100, c03 = 150,
K = Inf, N = Inf, S = -Inf,
steps1 = 0, stepm1 = 0.5, stepl1 = 0.8,
b1 = 1000, b2 = 2000, b3 = 3000)