N <- rbind(
c(10, 25),
c(10, 25),
c(10, 25),
c(10, 25),
c(10, 25)) # interim sample size and total sample size for each indication
scenarios <- rbind( c(0.15, 0.15, 0.15, 0.15, 0.15), c(0.3, 0.3, 0.3, 0.3, 0.3) )
res <- generate.data(N = N, ORRs = scenarios, ntrial = 1000, seed = 343809)
post <- post.infer(res, pnull = rep(0.15,5), stopbounds = cbind(c(1,1,1,1,1)),
ModelFit = "localPP", method = "PEB", a = 2, delta = 0.3)
(Q <- get.Q.bwer(post, alpha = 0.1, digits = 3, Qclust = rep(1, 5)))
(powers <- get.weighted.power(object = post, Q = Q))
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