utility_bias_binary {drugdevelopR} | R Documentation |
Utility function for bias adjustment programs with binary 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_binary()
function.
Usage
utility_binary_L(
n2,
RRgo,
Adj,
w,
p0,
p11,
p12,
in1,
in2,
alpha,
beta,
c2,
c3,
c02,
c03,
K,
N,
S,
steps1,
stepm1,
stepl1,
b1,
b2,
b3,
fixed
)
utility_binary_L2(
n2,
RRgo,
Adj,
w,
p0,
p11,
p12,
in1,
in2,
alpha,
beta,
c2,
c3,
c02,
c03,
K,
N,
S,
steps1,
stepm1,
stepl1,
b1,
b2,
b3,
fixed
)
utility_binary_R(
n2,
RRgo,
Adj,
w,
p0,
p11,
p12,
in1,
in2,
alpha,
beta,
c2,
c3,
c02,
c03,
K,
N,
S,
steps1,
stepm1,
stepl1,
b1,
b2,
b3,
fixed
)
utility_binary_R2(
n2,
RRgo,
Adj,
w,
p0,
p11,
p12,
in1,
in2,
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 |
RRgo |
threshold value for the go/no-go decision rule |
Adj |
adjustment parameter |
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 |
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 |
Value
The output of the functions utility_binary_L()
, utility_binary_L2()
, utility_binary_R()
and utility_binary_R2()
is the expected utility of the program.
Examples
res <- utility_binary_L(n2 = 50, RRgo = 0.8, Adj = 0.1, w = 0.3,
p0 = 0.6, p11 = 0.3, p12 = 0.5,
in1 = 300, in2 = 600,
alpha = 0.025, beta = 0.1,
c2 = 0.75, c3 = 1, c02 = 100, c03 = 150,
K = Inf, N = Inf, S = -Inf,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
fixed = TRUE)
res <- utility_binary_L2(n2 = 50, RRgo = 0.8, Adj = 0.1, w = 0.3,
p0 = 0.6, p11 = 0.3, p12 = 0.5,
in1 = 300, in2 = 600,
alpha = 0.025, beta = 0.1,
c2 = 0.75, c3 = 1, c02 = 100, c03 = 150,
K = Inf, N = Inf, S = -Inf,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
fixed = TRUE)
res <- utility_binary_R(n2 = 50, RRgo = 0.8, Adj = 0.9, w = 0.3,
p0 = 0.6, p11 = 0.3, p12 = 0.5,
in1 = 300, in2 = 600,
alpha = 0.025, beta = 0.1,
c2 = 0.75, c3 = 1, c02 = 100, c03 = 150,
K = Inf, N = Inf, S = -Inf,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
fixed = TRUE)
res <- utility_binary_R2(n2 = 50, RRgo = 0.8, Adj = 0.9, w = 0.3,
p0 = 0.6, p11 = 0.3, p12 = 0.5,
in1 = 300, in2 = 600,
alpha = 0.025, beta = 0.1,
c2 = 0.75, c3 = 1, c02 = 100, c03 = 150,
K = Inf, N = Inf, S = -Inf,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
fixed = TRUE)