utility_bias_binary: Utility function for bias adjustment programs with binary...

In Sterniii3/drugdevelopR: Utility-Based Optimal Phase II/III Drug Development Planning

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 p11 in terms of sample size
in2	amount of information for p12 in terms of sample size
alpha	significance level
beta	1-beta power for calculation of sample size for phase III
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 "small" in RR scale, default: 1
stepm1	lower boundary for effect size category "medium" in RR scale = upper boundary for effect size category "small" in RR scale, default: 0.95
stepl1	lower boundary for effect size category "large" in RR scale = upper boundary for effect size category "medium" in RR scale, default: 0.85
b1	expected gain for effect size category "small"
b2	expected gain for effect size category "medium"
b3	expected gain for effect size category "large"
fixed	choose if true treatment effects are fixed or random, if TRUE p11 is used as fixed effect

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)

Sterniii3/drugdevelopR documentation built on Jan. 26, 2024, 6:17 a.m.