Epgo_bias_binary | R Documentation |
In the case we do not only want do discount for overoptimistic results in phase II when calculating the sample size in phase III,
but also when deciding whether to go to phase III or not the functions Epgo_binary_L2
and Epgo_binary_R2
are necessary.
The function Epgo_binary_L2
uses an additive adjustment parameter (i.e. adjust the lower bound of the one-sided confidence interval),
the function Epgo_binary_R2
uses a multiplicative adjustment parameter (i.e. use estimate with a retention factor)
Epgo_binary_L2(RRgo, n2, Adj, p0, w, p11, p12, in1, in2, fixed)
Epgo_binary_R2(RRgo, n2, Adj, p0, w, p11, p12, in1, in2, fixed)
RRgo |
threshold value for the go/no-go decision rule |
n2 |
total sample size for phase II; must be even number |
Adj |
adjustment parameter |
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 |
The output of the functions Epgo_binary_L2
and Epgo_binary_R2
is the expected number of participants in phase III with conservative decision rule and sample size calculation.
res <- Epgo_binary_L2(RRgo = 0.8, n2 = 50, Adj = 0, p0 = 0.6, w = 0.3,
p11 = 0.3, p12 = 0.5, in1 = 300, in2 = 600,
fixed = FALSE)
res <- Epgo_binary_R2(RRgo = 0.8, n2 = 50, Adj = 1, p0 = 0.6, w = 0.3,
p11 = 0.3, p12 = 0.5, in1 = 300, in2 = 600,
fixed = FALSE)
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