utility_bias_normal {drugdevelopR} | R Documentation |
Utility function for bias adjustment 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 and the expected probability of a successful program.
The utility is in a further step maximized by the optimal_bias_normal()
function.
Usage
utility_normal_L(
n2,
kappa,
Adj,
w,
Delta1,
Delta2,
in1,
in2,
a,
b,
alpha,
beta,
c2,
c3,
c02,
c03,
K,
N,
S,
steps1,
stepm1,
stepl1,
b1,
b2,
b3,
fixed
)
utility_normal_L2(
n2,
kappa,
Adj,
w,
Delta1,
Delta2,
in1,
in2,
a,
b,
alpha,
beta,
c2,
c3,
c02,
c03,
K,
N,
S,
steps1,
stepm1,
stepl1,
b1,
b2,
b3,
fixed
)
utility_normal_R(
n2,
kappa,
Adj,
w,
Delta1,
Delta2,
in1,
in2,
a,
b,
alpha,
beta,
c2,
c3,
c02,
c03,
K,
N,
S,
steps1,
stepm1,
stepl1,
b1,
b2,
b3,
fixed
)
utility_normal_R2(
n2,
kappa,
Adj,
w,
Delta1,
Delta2,
in1,
in2,
a,
b,
alpha,
beta,
c2,
c3,
c02,
c03,
K,
N,
S,
steps1,
stepm1,
stepl1,
b1,
b2,
b3,
fixed
)
Arguments
n2 |
total sample size for phase II; must be even number |
kappa |
threshold value for the go/no-go decision rule |
Adj |
adjustment parameter |
w |
weight for mixture prior distribution |
Delta1 |
assumed true treatment effect for standardized difference in means |
Delta2 |
assumed true treatment effect for standardized difference in means |
in1 |
amount of information for |
in2 |
amount of information for |
a |
lower boundary for the truncation |
b |
upper boundary for the truncation |
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 |
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 |
b1 |
expected gain for effect size category |
b2 |
expected gain for effect size category |
b3 |
expected gain for effect size category |
fixed |
choose if true treatment effects are fixed or random, if TRUE Delta1 is used as fixed effect |
Value
The output of the functions utility_normal_L()
, utility_normal_L2()
, utility_normal_R()
and utility_normal_R2()
is the expected utility of the program.
Examples
res <- utility_normal_L(kappa = 0.1, n2 = 50, Adj = 0,
alpha = 0.025, beta = 0.1, w = 0.3,
Delta1 = 0.375, Delta2 = 0.625,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75,
c2 = 0.75, c3 = 1, c02 = 100, c03 = 150,
K = Inf, N = Inf, S = -Inf,
steps1 = 0, stepm1 = 0.5, stepl1 = 0.8,
b1 = 3000, b2 = 8000, b3 = 10000,
fixed = TRUE)
res <- utility_normal_L2(kappa = 0.1, n2 = 50, Adj = 0,
alpha = 0.025, beta = 0.1, w = 0.3,
Delta1 = 0.375, Delta2 = 0.625,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75,
c2 = 0.75, c3 = 1, c02 = 100, c03 = 150,
K = Inf, N = Inf, S = -Inf,
steps1 = 0, stepm1 = 0.5, stepl1 = 0.8,
b1 = 3000, b2 = 8000, b3 = 10000,
fixed = TRUE)
res <- utility_normal_R(kappa = 0.1, n2 = 50, Adj = 1,
alpha = 0.025, beta = 0.1, w = 0.3,
Delta1 = 0.375, Delta2 = 0.625,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75,
c2 = 0.75, c3 = 1, c02 = 100, c03 = 150,
K = Inf, N = Inf, S = -Inf,
steps1 = 0, stepm1 = 0.5, stepl1 = 0.8,
b1 = 3000, b2 = 8000, b3 = 10000,
fixed = TRUE)
res <- utility_normal_R2(kappa = 0.1, n2 = 50, Adj = 1,
alpha = 0.025, beta = 0.1, w = 0.3,
Delta1 = 0.375, Delta2 = 0.625,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75,
c2 = 0.75, c3 = 1, c02 = 100, c03 = 150,
K = Inf, N = Inf, S = -Inf,
steps1 = 0, stepm1 = 0.5, stepl1 = 0.8,
b1 = 3000, b2 = 8000, b3 = 10000,
fixed = TRUE)