drugdevelopR (version 1.0.2)

Ed3_bias: Expected sample size for phase III for bias adjustment programs and 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 functions Ed3_L(), Ed3_L2(), Ed3_R() and Ed3_R2(). Each function describes a specific case:

• Ed3_L(): calculates the optimal sample size 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

• Ed3_L2(): calculates the optimal sample size 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

• Ed3_R(): calculates the optimal sample size for a multiplicative adjustment factor (i.e. use estimate with a retention factor), however the go-decision is not affected by the bias adjustment

• Ed3_R2(): calculates the optimal sample size 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

Ed3_L(HRgo, d2, Adj, alpha, beta, w, hr1, hr2, id1, id2, fixed)
Ed3_L2(HRgo, d2, Adj, alpha, beta, w, hr1, hr2, id1, id2, fixed)
Ed3_R(HRgo, d2, Adj, alpha, beta, w, hr1, hr2, id1, id2, fixed)
Ed3_R2(HRgo, d2, Adj, alpha, beta, w, hr1, hr2, id1, id2, fixed)

Value

The output of the functions Ed3_L, Ed3_L2, Ed3_R and Ed3_R2 is the expected number of participants in phase III.

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

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 <-  Ed3_L(HRgo = 0.8, d2 = 50, Adj = 0.4,
                        alpha = 0.025, beta = 0.1, w = 0.3, 
                        hr1 =  0.69, hr2 = 0.81, 
                        id1 = 280, id2 = 420, fixed = FALSE)
          res <-  Ed3_L2(HRgo = 0.8, d2 = 50, Adj = 0.4,
                        alpha = 0.025, beta = 0.1, w = 0.3, 
                        hr1 =  0.69, hr2 = 0.81, 
                        id1 = 280, id2 = 420, fixed = FALSE)
          res <- Ed3_R(HRgo = 0.8, d2 = 50, Adj = 0.9,
                        alpha = 0.025, beta = 0.1, w = 0.3, 
                        hr1 =  0.69, hr2 = 0.81, 
                        id1 = 280, id2 = 420, fixed = FALSE)
          res <- Ed3_R2(HRgo = 0.8, d2 = 50, Adj = 0.9,
                        alpha = 0.025, beta = 0.1, w = 0.3, 
                        hr1 =  0.69, hr2 = 0.81, 
                        id1 = 280, id2 = 420, fixed = FALSE)