View source: R/functions_tte.R
prior_tte | R Documentation |
If we do not assume the treatment effects to be fixed, i.e. fixed = FALSE
,
the function prior_tte
allows us to model the treatment effect following a prior distribution.
For more details concerning the definition of a prior distribution, see the vignette on priors
as well as the Shiny app.
prior_tte(x, w, hr1, hr2, id1, id2)
x |
integration variable |
w |
weight for mixture prior distribution |
hr1 |
first assumed true treatment effect on HR scale for prior distribution |
hr2 |
second assumed true treatment effect on HR scale for prior distribution |
id1 |
amount of information for |
id2 |
amount of information for |
The output of the functions Epgo_tte()
is the expected number of participants in phase III with conservative decision rule and sample size calculation.
res <- prior_tte(x = 0.5, w = 0.5, hr1 = 0.69, hr2 = 0.88, id1 = 240, id2 = 420)
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