#load("finalSims.Rdata") library(VGAM) ### need to generate random Beta-Binomials. #### THIS FUNCTION GENERATES A RANDOM DATA SET #### use eff to change efficacy rate #### use longInd to make independent (check this) SHINE.genpts <- function(prop = c(0.20, 0.40, 0.40), pc = c(0.35, 0.25, 0.20), eff = 0, p.1m = matrix(c(0.125, 0.050, 0.021, 0.591, 0.640, 0.639), nrow=3, byrow=F), longInd = F){ pt <- pc + eff p.3m <- cbind(pc,pt) ##PTS #1 = accrual number #2 = NIHSS strata #3 = Treatment Group #4 = 3 Month Success Prob #5 = 3 Month Success (Y/N) #6 = 1 Month Success Prob #7 = 1 Month Success (Y/N) ##NIHSS baseline groups pts <- as.matrix(cbind(pt=1:1400, nihss=NA, trt=NA)) pts[,2] <- runif(n = 1400, min = 0, max = 1) pts[,2] <- ifelse(pts[,2] < prop[1], 1, ifelse(pts[,2] > (1 - prop[3]), 3, 2)) ## Randomize pts1 <- pts[pts[,2] == 1,] pts2 <- pts[pts[,2] == 2,] pts3 <- pts[pts[,2] == 3,] ##Assign to treatment groups - Even or odd if(dim(pts1)[[1]] %% 2){pts1[,3] <-c(rep(0:1, (dim(pts1)[[1]] - 1)/2),0)} else ##odd {pts1[,3] <-rep(0:1, dim(pts1)[[1]]/2)} ##even if(dim(pts1)[[1]] %% 2 & dim(pts2)[[1]] %% 2){pts2[,3] <-c(rep(1:0, (dim(pts2)[[1]] - 1)/2),1)} else if(dim(pts2)[[1]] %% 2){pts2[,3] <-c(rep(0:1, (dim(pts2)[[1]] - 1)/2),0)} else {pts2[,3] <-rep(0:1, dim(pts2)[[1]]/2)} if( (dim(pts3)[[1]] %% 2 & (dim(pts1)[[1]] %%2 & !dim(pts2)[[1]] %%2)) | (dim(pts3)[[1]] %% 2 & (!dim(pts1)[[1]] %%2 & dim(pts2)[[1]] %%2))) {pts3[,3] <-c(rep(1:0, (dim(pts3)[[1]] - 1)/2),1)} else if(dim(pts3)[[1]] %% 2){pts3[,3] <-c(rep(0:1, (dim(pts3)[[1]] - 1)/2),0)} else {pts3[,3] <-rep(0:1, dim(pts3)[[1]]/2)} pts <- rbind(pts1, pts2, pts3) pts <- cbind(pts, p.3m[cbind(pts[,2], pts[,3] +1)]) mo3 <- rbinom(n = 1400, size = 1, p = pts[,4]) pts <- cbind(pts, mo3) if(longInd==T){ p.1m <- cbind(pc*.85, pt*.85) pts <- cbind(pts, p.1m[cbind(pts[,2], pts[,3]+1)]) }else{ pts <- cbind(pts, p.1m[cbind(pts[,2], pts[,5]+1)]) } mo1 <- rbinom(n = 1400, size = 1, p = pts[,6]) pts <- cbind(pts, mo1) pts <- pts[order(pts[,1]),] pts[,3] <- pts[,3]+1 return(pts[, c(1:3, 7, 5)]) ## drops columns wiht prob of success } ### This function calculates the predictive probabilities of success ### with the current N & Max N SHINE.interim <- function(data, N, lag,look, alpha, sims=1000){ ### Data should have 5 columns: ptid, nihss class, trt, mo1, mo3 ## THIS are enrolled patients with complete data gr <- data[1:(N-lag),2] rd <- data[1:(N-lag),3] o1 <- data[1:(N-lag),4] o3 <- data[1:(N-lag),5] group <- data[,2] rand <- data[,3] out1 <- data[,4] out3 <- data[,5] ### crosstab of 1-month outcome by 3-month outcome by rand. tab1 <- table(factor(o1,levels=0:1), factor(o3,levels=0:1), factor(rd, levels=1:2)) tab1.ct <- tab1[,,1] ### just for control tab1.tr <- tab1[,,2] ### just for treatment ### crosstab of stroke severity by 3-month outcome by rand. tab2 <- table(factor(gr, levels=1:3), factor(o3, levels=0:1), factor(rd, levels=1:2)) tab2.ct <- tab2[,,1] tab2.tr <- tab2[,,2] ### crosstab of success by group tab3 <- table(factor(rd, levels=1:2), factor(o3,levels=0:1)) #### We have two types of outstanding patients: # those with 1 month follow-up (use rand group + 1mo mRS + tab1) lag.1 <- round(lag*2/3) # those without 1 mo follow-up (use rand gruop + baseline nihss + tab2) lag.2 <- lag-lag.1 # patients not yet enrolled (use rand group + tab0) lag.3 <- 1400-N ######### FIRST DO PRED PROB of WINNING IF STOPPING NOW ### First do prediction for those with 1 month l.1 <- table(factor(out1[(N-lag+1):(N-lag+lag.1)],levels=0:1), factor(rand[(N-lag+1):(N-lag+lag.1)], levels=1:2)) o1f.c <- rep(0,sims); o1s.c <- rep(0,sims); o1f.t <- rep(0,sims); o1s.t <- rep(0,sims) # Control group failures at one month that will be successes o1f.c <- if(l.1[1,1]>0){rbetabinom.ab(sims, l.1[1,1], 1+tab1.ct[1,2], 1+tab1.ct[1,1])} # Control group successes at one month that will be successes o1s.c <- if(l.1[2,1]>0){rbetabinom.ab(sims, l.1[2,1], 1+tab1.ct[2,2], 1+tab1.ct[2,1])} # Treatment group failures at one month that will be successes o1f.t <- if(l.1[1,2]>0){rbetabinom.ab(sims, l.1[1,2], 1+tab1.tr[1,2], 1+tab1.tr[1,1])} # Treatment group successes at one month that will be successes o1s.t <- if(l.1[2,2]>0){rbetabinom.ab(sims, l.1[2,2], 1+tab1.tr[2,2], 1+tab1.tr[2,1])} ### Second do prediction for those enrolled but with no data l.2 <- table(factor(group[(N-lag+lag.1+1):N], levels=1:3), factor(rand[(N-lag+lag.1+1):N], levels=1:2)) r1.c<-rep(0,sims); r2.c<-rep(0,sims); r3.c<-rep(0,sims) r1.t<-rep(0,sims); r2.t<-rep(0,sims); r3.t<-rep(0,sims) # Draw for each group NIHSS group x ctrl/trt (only draw if positive) if(l.2[1,1]>0){r1.c <- rbetabinom.ab(sims, l.2[1,1], 1+tab2.ct[1,2], 1+tab2.ct[1,1])} if(l.2[2,1]>0){r2.c <- rbetabinom.ab(sims, l.2[2,1], 1+tab2.ct[2,2], 1+tab2.ct[2,1])} if(l.2[3,1]>0){r3.c <- rbetabinom.ab(sims, l.2[3,1], 1+tab2.ct[3,2], 1+tab2.ct[3,1])} if(l.2[1,2]>0){r1.t <- rbetabinom.ab(sims, l.2[1,2], 1+tab2.tr[1,2], 1+tab2.tr[1,1])} if(l.2[2,2]>0){r2.t <- rbetabinom.ab(sims, l.2[2,2], 1+tab2.tr[2,2], 1+tab2.tr[2,1])} if(l.2[3,2]>0){r3.t <- rbetabinom.ab(sims, l.2[3,2], 1+tab2.tr[3,2], 1+tab2.tr[3,1])} ### Third do outstanding patients (those not yet enrolled) l.3 <- 700 - table(factor(rand[1:N], levels=1:2)) new.c <- rep(0, sims); new.t <- rep(0, sims) if(l.3[1]>0){new.c <- rbetabinom.ab(sims, l.3[1], 1+tab3[1,2], 1+tab3[1,1])} if(l.3[2]>0){new.t <- rbetabinom.ab(sims, l.3[2], 1+tab3[2,2], 1+tab3[2,1])} ## Total number of successes (observed + simulated) for currently enrolled pts total.ct.now <- tab3[1,2] + o1f.c + o1s.c + r1.c + r2.c + r3.c total.tr.now <- tab3[2,2] + o1f.t + o1s.t + r1.t + r2.t + r3.t ## total number of successes (observed + simulated for enrolled + simulated for future) total.ct.max <- total.ct.now + new.c total.tr.max <- total.tr.now + new.t pw.now <- post.win[,,look] pw.max <- post.win[,,10] post.now <- pw.now[cbind(total.ct.now, total.tr.now)] post.max <- pw.max[cbind(total.ct.max, total.tr.max)] Pn <- mean(post.now > (1-alpha)) Pmax <- mean(post.max > (1-alpha)) return(c(Pn,Pmax)) } ####################################### # RUN SIMS ####################################### SHINE.sims <- function(sims, eff, lag, Sn, Fn, alpha){ if(length(Sn)==1){Sn <- rep(Sn, 10)} if(length(Fn)==1){Fn <- rep(Fn, 10)} #output out <- matrix(nrow=sims, ncol=30) # columns of out: # BAYESIAN OCs # 1 N # 2 Looks # 3 Why Stop # 4-5 Nc, Nt # 6-7 Xc, Xt # 8-10 Nc NIHSS 1,2,3 # 11-13 Nt NIHSS 1,2,3 # 14-16 Xc NIHSS 1,2,3 # 17-19 Xt NIHSS 1,2,3 # 20 Pn @ stop # 21 Pmax @ stop # 22 Final Posterior of success # 23 Bayes Adaptive Win 0/1 # FREQUENTIST OCs # 24 N # 25 Why Stop # 26-27 Nc, Nt # 28-29 Xc, Xt # 30 Win for(s in 1:sims){ ## generate data data <- SHINE.genpts(eff=eff) ## Start interim looks at N=500 go <- 1 N <- 500; look <- 1 ## Run trial while(go==1){ pps <- SHINE.interim(data, N, lag,look, alpha, sims=1000) ## Check if PPnow > Sn if(pps[1] > Sn[look]){ go <- 0 why <- 3 }else if(pps[2] < Fn[look]){ go <- 0 why <- 1 }else if(look==9){ go <- 0 why <- 2 N <- 1400; look <- 10 }else{ go <- 1 N <- N+100 look <- look + 1 } } ### Summarize output out[s,1:3] <- c(look, N, why) out[s,4:5] <- table(factor(data[1:N,3], levels=1:2)) out[s,6:7] <- table(factor(data[1:N,3], levels=1:2), factor(data[1:N,5], levels=0:1))[,2] out[s,8:13] <- c(table(factor(data[1:N,2], levels=1:3), factor(data[1:N,3],levels=1:2))) out[s,14:19] <- c(table(factor(data[1:N,2], levels=1:3), factor(data[1:N,3],levels=1:2), factor(data[1:N,5], levels=0:1))[,,2]) out[s,20] <- ifelse(look==10, NA, pps[1]) out[s,21] <- ifelse(look==10, NA, pps[2]) out[s,22] <- post.win[out[s,6], out[s,7], look] out[s,23] <- ifelse(out[s,22] >= (1-alpha), 1, 0) ### Do frequentist trial # Futility look at 700 tab.fut <- table(factor(data[1:700,3], levels=1:2), factor(data[1:700,5], levels=0:1)) tab.suc <- table(factor(data[1:938,3], levels=1:2), factor(data[1:938,5], levels=0:1)) tab.fin <- table(factor(data[,3], levels=1:2), factor(data[,5], levels=0:1)) if(tab.fut[2,2] <= fut.stop[tab.fut[1,2]]){ out[s,24] <- 700+lag out[s,25] <- 1 out[s,26:27] <- apply(tab.fut, 1, sum) out[s,28:29] <- tab.fut[,2] out[s,30] <- 0 }else if(tab.suc[2,2] >= suc.stop[tab.suc[1,2]]){ out[s,24] <- 938+lag out[s,25] <- 3 out[s,26:27] <- apply(tab.suc, 1, sum) out[s,28:29] <- tab.suc[,2] out[s,30] <- 1 }else{ out[s,24] <- 1400 out[s,25] <- 2 out[s,26:27] <- apply(tab.fin, 1, sum) out[s,28:29] <- tab.fin[,2] out[s,30] <- ifelse(tab.fin[2,2]>=final.stop[tab.fin[1,2]], 1, 0) } # cat(s) } return(out) } Sn <- rep( c(0.99,0.95,0.90), c(4,3,3)) Fn <- rep( c(0.001,0.01, 0.025), c(4,3,3)) ####### Now run efficacy scenarios p <- 0.018 out.n2.018a <- SHINE.sims(sims=10000, eff=-0.02, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.n1.018a <- SHINE.sims(sims=10000, eff=-0.01, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.00.018a <- SHINE.sims(sims=10000, eff= 0.00, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.01.018a <- SHINE.sims(sims=10000, eff= 0.01, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.02.018a <- SHINE.sims(sims=10000, eff= 0.02, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.03.018a <- SHINE.sims(sims=10000, eff= 0.03, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.04.018a <- SHINE.sims(sims=10000, eff= 0.04, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.05.018a <- SHINE.sims(sims=10000, eff= 0.05, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.06.018a <- SHINE.sims(sims=10000, eff= 0.06, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.07.018a <- SHINE.sims(sims=10000, eff= 0.07, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.08.018a <- SHINE.sims(sims=10000, eff= 0.08, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.09.018a <- SHINE.sims(sims=10000, eff= 0.09, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.10.018a <- SHINE.sims(sims=10000, eff= 0.10, lag=100, Sn=Sn, Fn=Fn, alpha=p) p <- 0.018 out.n2.018b <- SHINE.sims(sims=10000, eff=-0.02, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.n1.018b <- SHINE.sims(sims=10000, eff=-0.01, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.00.018b <- SHINE.sims(sims=10000, eff= 0.00, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.01.018b <- SHINE.sims(sims=10000, eff= 0.01, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.02.018b <- SHINE.sims(sims=10000, eff= 0.02, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.03.018b <- SHINE.sims(sims=10000, eff= 0.03, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.04.018b <- SHINE.sims(sims=10000, eff= 0.04, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.05.018b <- SHINE.sims(sims=10000, eff= 0.05, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.06.018b <- SHINE.sims(sims=10000, eff= 0.06, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.07.018b <- SHINE.sims(sims=10000, eff= 0.07, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.08.018b <- SHINE.sims(sims=10000, eff= 0.08, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.09.018b <- SHINE.sims(sims=10000, eff= 0.09, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.10.018b <- SHINE.sims(sims=10000, eff= 0.10, lag=100, Sn=Sn, Fn=Fn, alpha=p) p <- 0.018 out.n2.018c <- SHINE.sims(sims=10000, eff=-0.02, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.n1.018c <- SHINE.sims(sims=10000, eff=-0.01, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.00.018c <- SHINE.sims(sims=10000, eff= 0.00, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.01.018c <- SHINE.sims(sims=10000, eff= 0.01, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.02.018c <- SHINE.sims(sims=10000, eff= 0.02, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.03.018c <- SHINE.sims(sims=10000, eff= 0.03, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.04.018c <- SHINE.sims(sims=10000, eff= 0.04, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.05.018c <- SHINE.sims(sims=10000, eff= 0.05, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.06.018c <- SHINE.sims(sims=10000, eff= 0.06, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.07.018c <- SHINE.sims(sims=10000, eff= 0.07, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.08.018c <- SHINE.sims(sims=10000, eff= 0.08, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.09.018c <- SHINE.sims(sims=10000, eff= 0.09, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.10.018c <- SHINE.sims(sims=10000, eff= 0.10, lag=100, Sn=Sn, Fn=Fn, alpha=p) p <- 0.018 out.n2.018d <- SHINE.sims(sims=10000, eff=-0.02, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.n1.018d <- SHINE.sims(sims=10000, eff=-0.01, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.00.018d <- SHINE.sims(sims=10000, eff= 0.00, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.01.018d <- SHINE.sims(sims=10000, eff= 0.01, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.02.018d <- SHINE.sims(sims=10000, eff= 0.02, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.03.018d <- SHINE.sims(sims=10000, eff= 0.03, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.04.018d <- SHINE.sims(sims=10000, eff= 0.04, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.05.018d <- SHINE.sims(sims=10000, eff= 0.05, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.06.018d <- SHINE.sims(sims=10000, eff= 0.06, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.07.018d <- SHINE.sims(sims=10000, eff= 0.07, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.08.018d <- SHINE.sims(sims=10000, eff= 0.08, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.09.018d <- SHINE.sims(sims=10000, eff= 0.09, lag=100, Sn=Sn, Fn=Fn, alpha=p) out.10.018d <- SHINE.sims(sims=10000, eff= 0.10, lag=100, Sn=Sn, Fn=Fn, alpha=p) effect <- seq(-0.02, 10, by=0.01) out.018.n2 <- rbind(out.n2.018a, out.n2.018b, out.n2.018c, out.n2.018d) out.018.n1 <- rbind(out.n1.018a, out.n1.018b, out.n1.018c, out.n1.018d) out.018.00 <- rbind(out.00.018a, out.00.018b, out.00.018c, out.00.018d) out.018.01 <- rbind(out.01.018a, out.01.018b, out.01.018c, out.01.018d) out.018.02 <- rbind(out.02.018a, out.02.018b, out.02.018c, out.02.018d) out.018.03 <- rbind(out.03.018a, out.03.018b, out.03.018c, out.03.018d) out.018.04 <- rbind(out.04.018a, out.04.018b, out.04.018c, out.04.018d) out.018.05 <- rbind(out.05.018a, out.05.018b, out.05.018c, out.05.018d) out.018.06 <- rbind(out.06.018a, out.06.018b, out.06.018c, out.06.018d) out.018.07 <- rbind(out.07.018a, out.07.018b, out.07.018c, out.07.018d) out.018.08 <- rbind(out.08.018a, out.08.018b, out.08.018c, out.08.018d) out.018.09 <- rbind(out.09.018a, out.09.018b, out.09.018c, out.09.018d) out.018.10 <- rbind(out.10.018a, out.10.018b, out.10.018c, out.10.018d) #### Can do summary statistics for each set now 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