drugdevelopR (version 1.0.2)

utility_multiple_normal: Utility function for multiple endpoints 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_multiple_normal() function.

Usage

utility_multiple_normal(
  kappa,
  n2,
  alpha,
  beta,
  Delta1,
  Delta2,
  in1,
  in2,
  sigma1,
  sigma2,
  c2,
  c02,
  c3,
  c03,
  K,
  N,
  S,
  steps1,
  stepm1,
  stepl1,
  b1,
  b2,
  b3,
  fixed,
  rho,
  relaxed,
  rsamp
)

Value

The output of the function utility_multiple_normal() is the expected utility of the program.

Arguments

kappa

threshold value for the go/no-go decision rule; vector for both endpoints

n2

total sample size for phase II; must be even number

alpha

significance level

beta

1-beta power for calculation of sample size for phase III

Delta1

assumed true treatment effect given as difference in means for endpoint 1

Delta2

assumed true treatment effect given as difference in means for endpoint 2

in1

amount of information for Delta1 in terms of sample size

in2

amount of information for Delta2 in terms of sample size

sigma1

standard deviation of first endpoint

sigma2

standard deviation of second endpoint

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" in HR scale, default: 1

stepm1

lower boundary for effect size category "medium" in HR scale = upper boundary for effect size category "small" in HR scale, default: 0.95

stepl1

lower boundary for effect size category "large" in HR scale = upper boundary for effect size category "medium" in HR 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 Delta1 is used as fixed effect

rho

correlation between the two endpoints

relaxed

relaxed or strict decision rule