drugdevelopR (version 1.0.2)

utility_tte: Utility function for time-to-event 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_tte() function.

Usage

utility_tte(
  d2,
  HRgo,
  w,
  hr1,
  hr2,
  id1,
  id2,
  alpha,
  beta,
  xi2,
  xi3,
  c2,
  c3,
  c02,
  c03,
  K,
  N,
  S,
  steps1,
  stepm1,
  stepl1,
  b1,
  b2,
  b3,
  gamma,
  fixed
)

Value

The output of the functions utility_tte() is the expected utility of the program.

Arguments

d2

total events for phase II; must be even number

HRgo

threshold value for the go/no-go decision rule

w

weight for mixture prior distribution

hr1

first assumed true treatment effect on HR scale for prior distribution

hr2

second assumed true treatment effect on HR scale for prior distribution

id1

amount of information for hr1 in terms of number of events

id2

amount of information for hr2 in terms of number of events

alpha

significance level

beta

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

xi2

event rate for phase II

xi3

event rate 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"

gamma

difference in treatment effect due to different population structures in phase II and III

fixed

choose if true treatment effects are fixed or random, if TRUE hr1 is used as fixed effect

Examples

res <- utility_tte(d2 = 50, HRgo = 0.8, w = 0.3, 
                                 hr1 =  0.69, hr2 = 0.81, 
                                 id1 = 280, id2 = 420, xi2 = 0.7, xi3 = 0.7,
                                 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, 
                                 gamma = 0, fixed = TRUE)