En3_bias_binary {drugdevelopR} | R Documentation |
Expected sample size for phase III for bias adjustment programs and binary 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_binary_L()
, En3_binary_L2()
, En3_binary_R()
and En3_binary_R2()
.
Each function describes a specific case:
-
En3_binary_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_binary_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_binary_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_binary_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_binary_L(RRgo, n2, Adj, alpha, beta, p0, w, p11, p12, in1, in2, fixed)
En3_binary_L2(RRgo, n2, Adj, alpha, beta, p0, w, p11, p12, in1, in2, fixed)
En3_binary_R(RRgo, n2, Adj, alpha, beta, p0, w, p11, p12, in1, in2, fixed)
En3_binary_R2(RRgo, n2, Adj, alpha, beta, p0, w, p11, p12, in1, in2, fixed)
Arguments
RRgo |
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 |
|
p0 |
assumed true rate of control group |
w |
weight for mixture prior distribution |
p11 |
assumed true rate of treatment group |
p12 |
assumed true rate of treatment group |
in1 |
amount of information for |
in2 |
amount of information for |
fixed |
choose if true treatment effects are fixed or random, if TRUE |
Value
The output of the functions En3_binary_L
, En3_binary_L2
, En3_binary_R
and En3_binary_R2
is the expected number of participants in phase III.
Examples
res <- En3_binary_L(RRgo = 0.8, n2 = 50, Adj = 0,
alpha = 0.025, beta = 0.1, p0 = 0.6, w = 0.3,
p11 = 0.3, p12 = 0.5, in1 = 300, in2 = 600,
fixed = FALSE)
res <- En3_binary_L2(RRgo = 0.8, n2 = 50, Adj = 0,
alpha = 0.025, beta = 0.1, p0 = 0.6, w = 0.3,
p11 = 0.3, p12 = 0.5, in1 = 300, in2 = 600,
fixed = FALSE)
res <- En3_binary_R(RRgo = 0.8, n2 = 50, Adj = 1,
alpha = 0.025, beta = 0.1, p0 = 0.6, w = 0.3,
p11 = 0.3, p12 = 0.5, in1 = 300, in2 = 600,
fixed = FALSE)
res <- En3_binary_R2(RRgo = 0.8, n2 = 50, Adj = 1,
alpha = 0.025, beta = 0.1, p0 = 0.6, w = 0.3,
p11 = 0.3, p12 = 0.5, in1 = 300, in2 = 600,
fixed = FALSE)