En3_bias_normal {drugdevelopR} | R Documentation |
Expected sample size for phase III for bias adjustment programs and normally distributed 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 En3_normal_L()
, En3_normal_L2()
, En3_normal_R()
and En3_normal_R2()
.
Each function describes a specific case:
-
En3_normal_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 -
En3_normal_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 -
En3_normal_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 -
En3_normal_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
En3_normal_L(
kappa,
n2,
Adj,
alpha,
beta,
w,
Delta1,
Delta2,
in1,
in2,
a,
b,
fixed
)
En3_normal_L2(
kappa,
n2,
Adj,
alpha,
beta,
w,
Delta1,
Delta2,
in1,
in2,
a,
b,
fixed
)
En3_normal_R(
kappa,
n2,
Adj,
alpha,
beta,
w,
Delta1,
Delta2,
in1,
in2,
a,
b,
fixed
)
En3_normal_R2(
kappa,
n2,
Adj,
alpha,
beta,
w,
Delta1,
Delta2,
in1,
in2,
a,
b,
fixed
)
Arguments
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 |
|
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 |
Value
The output of the functions En3_normal_L
, En3_normal_L2
, En3_normal_R
and En3_normal_R2
is the expected number of participants in phase III.
Examples
res <- En3_normal_L(kappa = 0.1, n2 = 50, Adj = 0,
alpha = 0.025, beta = 0.1, w = 0.3,
Delta1 = 0.375, Delta2 = 0.625,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75, fixed = FALSE)
res <- En3_normal_L2(kappa = 0.1, n2 = 50, Adj = 0,
alpha = 0.025, beta = 0.1, w = 0.3,
Delta1 = 0.375, Delta2 = 0.625,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75, fixed = TRUE)
res <- En3_normal_R(kappa = 0.1, n2 = 50, Adj = 1,
alpha = 0.025, beta = 0.1, w = 0.3,
Delta1 = 0.375, Delta2 = 0.625,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75, fixed = FALSE)
res <- En3_normal_R2(kappa = 0.1, n2 = 50, Adj = 1,
alpha = 0.025, beta = 0.1, w = 0.3,
Delta1 = 0.375, Delta2 = 0.625,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75, fixed = FALSE)