drugdevelopR (version 1.0.2)

utility_multiarm_binary: Utility function for multiarm 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 as on the sample size and expected probability of a successful program. The utility is in further step maximized by the optimal_multiarm_binary() function.

Usage

utility_multiarm_binary(
  n2,
  RRgo,
  alpha,
  beta,
  p0 = p0,
  p11 = p11,
  p12 = p12,
  strategy,
  c2,
  c02,
  c3,
  c03,
  K,
  N,
  S,
  steps1,
  stepm1,
  stepl1,
  b1,
  b2,
  b3
)

Value

The output of the function utility_multiarm_binary() is the expected utility of the program

Arguments

n2

total sample size for phase II; must be even number

RRgo

threshold value for the go/no-go decision rule

alpha

significance level

beta

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

p0

assumed true rate of control group

p11

assumed true rate of treatment group

p12

assumed true rate of treatment group

strategy

choose Strategy: 1 ("only best promising"), 2 ("all promising")

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 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"

Examples

res <- utility_multiarm_binary(n2 = 50, RRgo = 0.8, alpha = 0.05, beta = 0.1,
                            p0 = 0.6, p11 =  0.3, p12 = 0.5, strategy = 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)