drugdevelopR (version 1.0.2)

EPsProg_bias: Expected probability of a successful program for bias adjustment programs with time-to-event outcomes

Description

To discount for overoptimistic results in phase II when calculating the optimal sample size in phase III, it is necessary to use the following functions, which each describe a specific case:

• EPsProg_L(): calculates the expected probability of a successful for an additive adjustment factor (i.e. adjust the lower bound of the one-sided confidence interval), however the go-decision is not affected by the bias adjustment

• EPsProg_L2(): calculates the expected probability of a successful for an additive adjustment factor (i.e. adjust the lower bound of the one-sided confidence interval) when the go-decision is also affected by the bias adjustment

• EPsProg_R(): calculates the expected probability of a successful for a multiplicative adjustment factor (i.e. use estimate with a retention factor), however the go-decision is not affected by the bias adjustment

• EPsProg_R2(): calculates the expected probability of a successful for a multiplicative adjustment factor (i.e. use estimate with a retention factor) when the go-decision is also affected by the bias adjustment

Usage

EPsProg_L(
  HRgo,
  d2,
  Adj,
  alpha,
  beta,
  step1,
  step2,
  w,
  hr1,
  hr2,
  id1,
  id2,
  fixed
)
EPsProg_L2(
  HRgo,
  d2,
  Adj,
  alpha,
  beta,
  step1,
  step2,
  w,
  hr1,
  hr2,
  id1,
  id2,
  fixed
)
EPsProg_R(
  HRgo,
  d2,
  Adj,
  alpha,
  beta,
  step1,
  step2,
  w,
  hr1,
  hr2,
  id1,
  id2,
  fixed
)
EPsProg_R2(
  HRgo,
  d2,
  Adj,
  alpha,
  beta,
  step1,
  step2,
  w,
  hr1,
  hr2,
  id1,
  id2,
  fixed
)

Value

The output of the functions EPsProg_L(), EPsProg_L2(), EPsProg_R() and EPsProg_R2() is the expected probability of a successful program.

Arguments

HRgo

threshold value for the go/no-go decision rule

d2

total events for phase II; must be even number

Adj

adjustment parameter

alpha

significance level

beta

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

step1

lower boundary for effect size

step2

upper boundary for effect size

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

fixed

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

Examples

res <- EPsProg_L(HRgo = 0.8, d2 = 50, Adj = 0.4, 
                           alpha = 0.025, beta = 0.1, 
                           step1 = 1, step2 = 0.95, 
                           w = 0.3, hr1 = 0.69, hr2 = 0.81,
                           id1 = 280, id2 = 420, fixed = FALSE)
          res <- EPsProg_L2(HRgo = 0.8, d2 = 50, Adj = 0.4, 
                           alpha = 0.025, beta = 0.1, 
                           step1 = 1, step2 = 0.95, 
                           w = 0.3, hr1 = 0.69, hr2 = 0.81,
                           id1 = 280, id2 = 420, fixed = FALSE)
          res <- EPsProg_R(HRgo = 0.8, d2 = 50, Adj = 0.9, 
                           alpha = 0.025, beta = 0.1, 
                           step1 = 1, step2 = 0.95, 
                           w = 0.3, hr1 = 0.69, hr2 = 0.81,
                           id1 = 280, id2 = 420, fixed = FALSE)
          res <- EPsProg_R2(HRgo = 0.8, d2 = 50, Adj = 0.9, 
                           alpha = 0.025, beta = 0.1, 
                           step1 = 1, step2 = 0.95, 
                           w = 0.3, hr1 = 0.69, hr2 = 0.81,
                           id1 = 280, id2 = 420, fixed = FALSE)