Ed3_bias | R Documentation |
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
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)
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 |
|
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 |
The output of the functions Ed3_L
, Ed3_L2
, Ed3_R
and Ed3_R2
is the expected number of participants in phase III.
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)
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