Nothing
## ----include = FALSE------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 7,
fig.height = 5
)
## ----setup, include=FALSE-------------------------------------------
library(adoptr)
## -------------------------------------------------------------------
datadist <- Binomial(0.3, two_armed = TRUE)
## -------------------------------------------------------------------
H_0 <- PointMassPrior(.0, 1)
prior <- ContinuousPrior(function(x) 1 / (pnorm(0.69, 0.2, 0.2) -
pnorm(-0.29, 0.2, 0.2)) *
dnorm(x, 0.2, 0.2),
support = c(-0.29,0.69),
tighten_support = TRUE)
## -------------------------------------------------------------------
alpha <- 0.025
min_epower <- 0.8
toer_cnstr <- Power(datadist, H_0) <= alpha
epow_cnstr <- Power(datadist, condition(prior, c(0.0,0.69))) >= min_epower
## -------------------------------------------------------------------
ess <- ExpectedSampleSize(datadist,prior)
init <- get_initial_design(0.2,0.025,0.2)
opt_design <- minimize(ess,subject_to(toer_cnstr,epow_cnstr),
initial_design = init, check_constraints = TRUE)
plot(opt_design$design)
## -------------------------------------------------------------------
datadist <- Survival(0.7, two_armed = TRUE)
## -------------------------------------------------------------------
H_0 <- PointMassPrior(1, 1)
H_1 <- PointMassPrior(1.7, 1)
## -------------------------------------------------------------------
alpha <- 0.025
min_power <- 0.8
toer_con <- Power(datadist,H_0) <= alpha
pow_con <- Power(datadist,H_1) >= min_power
## -------------------------------------------------------------------
exp_no_events <- ExpectedNumberOfEvents(datadist, H_1)
init <- get_initial_design(1.7, 0.025, 0.2, dist=datadist)
opt_survival <- minimize(exp_no_events, subject_to(toer_con,pow_con),
initial_design = init, check_constraints=TRUE)
summary(opt_survival$design)
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