EPsProg_bias_normal {drugdevelopR} | R Documentation |
Expected probability of a successful program for bias adjustment programs with 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 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
Usage
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
)
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 |
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 |
Value
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.
Examples
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)