utility_multiple_normal: Utility function for multiple endpoints with normally...

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

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
)

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

Value

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

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