## ----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)