EPsProg_bias {drugdevelopR} | R Documentation |
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
)
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 |
|
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 |
id2 |
amount of information for |
fixed |
choose if true treatment effects are fixed or random, if TRUE |
Value
The output of the functions EPsProg_L()
, EPsProg_L2()
, EPsProg_R()
and EPsProg_R2()
is the expected probability of a successful program.
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)