EPsProg_bias_normal | 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 following functions, which each describe a specific case:
EPsProg_normal_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_normal_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_normal_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_normal_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
EPsProg_normal_L(
kappa,
n2,
Adj,
alpha,
beta,
step1,
step2,
w,
Delta1,
Delta2,
in1,
in2,
a,
b,
fixed
)
EPsProg_normal_L2(
kappa,
n2,
Adj,
alpha,
beta,
step1,
step2,
w,
Delta1,
Delta2,
in1,
in2,
a,
b,
fixed
)
EPsProg_normal_R(
kappa,
n2,
Adj,
alpha,
beta,
step1,
step2,
w,
Delta1,
Delta2,
in1,
in2,
a,
b,
fixed
)
EPsProg_normal_R2(
kappa,
n2,
Adj,
alpha,
beta,
step1,
step2,
w,
Delta1,
Delta2,
in1,
in2,
a,
b,
fixed
)
kappa |
threshold value for the go/no-go decision rule |
n2 |
total sample size 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 |
Delta1 |
assumed true treatment effect for standardized difference in means |
Delta2 |
assumed true treatment effect for standardized difference in means |
in1 |
amount of information for |
in2 |
amount of information for |
a |
lower boundary for the truncation |
b |
upper boundary for the truncation |
fixed |
choose if true treatment effects are fixed or random, if TRUE |
The output of the functions EPsProg_normal_L()
, EPsProg_normal_L2()
, EPsProg_normal_R()
and EPsProg_normal_R2()
is the expected probability of a successful program.
res <- EPsProg_normal_L(kappa = 0.1, n2 = 50, Adj = 0,
alpha = 0.025, beta = 0.1, w = 0.3,
step1 = 0, step2 = 0.5,
Delta1 = 0.375, Delta2 = 0.625,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75, fixed = FALSE)
res <- EPsProg_normal_L2(kappa = 0.1, n2 = 50, Adj = 0,
alpha = 0.025, beta = 0.1, w = 0.3,
step1 = 0, step2 = 0.5,
Delta1 = 0.375, Delta2 = 0.625,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75, fixed = FALSE)
res <- EPsProg_normal_R(kappa = 0.1, n2 = 50, Adj = 1,
alpha = 0.025, beta = 0.1, w = 0.3,
step1 = 0, step2 = 0.5,
Delta1 = 0.375, Delta2 = 0.625,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75, fixed = FALSE)
res <- EPsProg_normal_R2(kappa = 0.1, n2 = 50, Adj = 1,
alpha = 0.025, beta = 0.1, w = 0.3,
step1 = 0, step2 = 0.5,
Delta1 = 0.375, Delta2 = 0.625,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75, fixed = FALSE)
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