R/Fleming2stage.R
In IDDI-BE/PhIIdesign: Sample Size Calculation for Phase II Designs
Defines functions
fleming2stage.default
fleming2stage
P_Fleming2st_reject_Ha
Documented in
fleming2stage
#------------------------------------------------------#
# Equation from paper Mander and Thompson: Section 2.2 #
#------------------------------------------------------#
P_Fleming2st_reject_Ha <- function(n1, n2, r, a, b_p0, B_p0, b_pa, B_pa){
## r is the total number of seen cases on both n1 and n2 together
## Calculate alpha/beta for all values up to r
r_1 <- (max(0,r-n2)):(max(0,min(a-2, r-1))) # for min: - there should be x's for which r1<x1<a, so minimum for a should be 2
# - if r>n2, and knowing that you must have (r+1) successes to reject H0 after stage 2,
# then you must already have >= (r+1)-n2 successes in stage 1 with continuation, so r1 should be minimal
# (r-n2), - because contination happens when N(responses) is r1+1 -and with one x1 between r1 and a,
# the minimal value for a is thus (r-n2)+2
# for max: r1<=a-2, as there must be at least one value x1, for which r1<x1<a, so trial can continue to stage 2
# also logical that r1<=r-1, as you need r1+1 to proceed to second stage.
# if r1=r than in second stage X1+X2>r, so second stage not needed anymore
x1 <- r_1 + 1
# calculate eta_temp=P(not rejecting H0) under H0 for each (r1,x1)
#-----------------------------------------------------------------
B_p0_r1 <- B_p0[[n1]][1 + r_1] # P(X1<=r1|n1,p0) so pbinom(r_1 ,n1,p0), indexing with "+1", as B_p0 starts from 0 successes
b_p0_x1 <- b_p0[[n1]][1 + x1] # P(X1=x1|n1,p0) so dbinom(x1 ,n1,p0), indexing with "+1", as b_p0 starts from 0 successes
B_p0_r2 <- B_p0[[n2]][1 + (r-x1)] # P(X2<=r-x1|n2,p0) so pbinom(r-x1,n2,p0), indexing with "+1", as B_p0 starts from 0 successes
eta_temp <- B_p0_r1 + rev(cumsum(rev(b_p0_x1 * B_p0_r2))) # e.g. for r=2: r_1=0: P(X1<=0) + P(X1=1)P(X2<=2-1) + P(X1=2)P(X2<=2-2)
# r_1=1: P(X1<=1) + P(X1=2)P(X2<=2-2)
# calculate beta_temp=P(not rejecting H0) under Ha for each (r1,x1)
#------------------------------------------------------------------
B_pa_r1 <- B_pa[[n1]][1 + r_1]
b_pa_x1 <- b_pa[[n1]][1 + x1]
B_pa_r2 <- B_pa[[n2]][1 + (r-x1)]
beta_temp <- B_pa_r1 + rev(cumsum(rev(b_pa_x1 * B_pa_r2)))
list(N = n1 + n2, n1 = n1, r1 = r_1, a=a, n2 = n2, r2 = r, alpha_temp = 1 - eta_temp, beta_temp = beta_temp)
}
#' @title Fleming 2-stage function
#' @description
#' The primary objective of a phase II clinical trial of a new drug or regimen is to determine whether it has sufficient biological activity
#' against the disease under study to warrant more extensive development. \cr
#' This function calculates the sample size needed in a Fleming 2-stage design which is a
#' two-stage design that is optimal in the sense that the expected sample size is minimized if the
#' regimen has low activity subject to constraints upon the size of the type 1 and type 2 errors. \cr
#' Two-stage designs which minimize the maximum sample size are also determined. \cr
#' This type of design also allows stopping for efficacy
#' @param p0 probability of the uninteresting response (null hypothesis \eqn{H0})
#' @param pa probability of the interesting response (alternative hypothesis Ha)
#' @param alpha Type I error rate \eqn{P(reject H0|H0)}
#' @param beta Type II error rate \eqn{P(reject Ha|Ha)}
#' @param eps tolerance default value = 0.005
#' @param N_min minimum sample size value for grid search
#' @param N_max maximum sample size value for grid search
#' @param int pre-specified interim analysis percentage information
#' @param int_window window around interim analysis percentage (e.g. 0.5 +- 0.025). 0.025 is default value
#' @param opt_under optimality under "H0" or "Ha"
#' @param CI_type "Koyama", see \code{\link{getCI_Koyama}}, or any type for \link[binom]{binom.confint}
#' @return a data.frame with elements
#' \itemize{
#' \item n1: total number of patients in stage1
#' \item n2: total number of patients in stage2
#' \item N: total number of patients=n1+n2
#' \item r1: ("r" stands for "rejection") threshold for "rejecting" Ha: if x1<=r1 --> stop for futility at first stage
#' \item r2: ("r" stands for "rejection") threshold for "rejecting" Ha: if x1+x2<=r --> futility at second stage
#' \item a: ("a" for "acceptance") threshold for "accepting" Ha= stop for efficacy at first stage
#' \item eff: (r2 + 1)/N
#' \item CI_LL: (1-2*alpha) CI lower limit
#' \item CI_UL: (1-2*alpha) CI upper limit
#' \item EN.p0: expected sample size under H0
#' \item PET.p0: probability of terminating the trial for futility at the end of the first stage under H0
#' \item EN.pa: expected sample size under Ha
#' \item PET.pa: probability of terminating the trial for efficacy at the end of the first stage under Ha
#' \item MIN: column indicating if the design is the minimal design
#' \item OPT: column indicating if the setting is the optimal design
#' \item ADMISS: column indicating if the setting is the admissible design
#' \item alpha: the actual alpha value which is smaller than \code{alpha_param + eps}
#' \item beta: the actual beta value where which is smaller than \code{beta_param + eps}
#' \item p0: your provided \code{p0} value
#' \item pa: your provided \code{pa} value
#' \item alpha_param: your provided \code{alpha} value
#' \item beta_param: your provided \code{beta} value
#' }
#' @details
#' if x1<=r1 --> stop futility at first stage \cr
#' if r1<x1<a --> proceed to second dtage \cr
#' if x1>=a --> stop efficacy at first stage \cr
#' if (x1+x2)<=r2 --> futility at second stage \cr
#' if (x1+x2)> r2 --> efficacy at second stage \cr
#' So PET.H0=P_H0(X1<=r1)+P_H0(X1>=a1)
#' @references Mander AP, Thompson SG. Two-stage designs optimal under the alternative hypothesis for phase II cancer clinical trials.
#' Contemporary Clinical Trials 2010;31:572–578
#' Qin F et al. Optimal, minimax and admissible two-stage design for phase II oncology clinical trials. BMC Medical Research Methodology 2020;20:126
#' @export
#' @examples
#' result_H0 <-fleming2stage(p0 = 0.3, pa = 0.5, alpha = 0.1, beta = 0.1, eps = 0, N_min = 3,
#' N_max = 50, opt_under="H0")
#' result_Ha <-fleming2stage(p0 = 0.3, pa = 0.5, alpha = 0.1, beta = 0.1, eps = 0, N_min = 3,
#' N_max = 50, opt_under="Ha")
fleming2stage <- function(p0, pa, alpha, beta, eps = 0, N_min, N_max, int=0, int_window=0.025, opt_under="H0", CI_type="Koyama"){
if(length(p0) > 1 && length(pa) > 1){
results <- mapply(null = p0, alternative = pa, alpha = alpha, beta = beta, eps = eps, N_min = N_min, N_max = N_max, int = int, int_window = int_window, opt_under = opt_under, CI_type=CI_type,
FUN = function(null, alternative, alpha, beta, eps, N_min, N_max, int, int_window){
fleming2stage.default(p0 = null, pa = alternative, alpha = alpha, beta = beta, eps = eps, N_min = N_min, N_max = N_max, int = int, int_window = int_window, opt_under = opt_under, CI_type=CI_type)
}, SIMPLIFY = FALSE)
}else{
results <- fleming2stage.default(p0 = p0, pa = pa, alpha = alpha, beta = beta, eps = eps, N_min = N_min, N_max = N_max, int = int, int_window = int_window, opt_under = opt_under, CI_type=CI_type)
}
}
fleming2stage.default <- function(p0, pa, alpha, beta, eps = 0, N_min, N_max, int=0, int_window=0.025, opt_under="H0", CI_type="Koyama") {
# WARNING MESSAGES
if (pa < p0) {
stop("p0 should be smaller than pa")
}
if (N_min <3 ) {
stop("N_min should be >=3")
}
EN.p0 <- EN.p <- EN.p_N_min <- EN.p_N_n1_min <- EN.p_min <- EN.p_N_min_int <- EN.pa <- MIN <- N <- OPT <- NULL
rowid <- n1 <- n2 <- r <- r2 <- a <- NULL
fullresult<-list()
#----------------------------------------------------------------------------------------------------------#
# Get all possible scenarios for N, n1, r1, r2 and n2 #
# Note that this is the possible range of values: 0 <=r1<n1; r1+1<=r; r<=n1+n2 #
#----------------------------------------------------------------------------------------------------------#
# Get for selected N's all possible n1's (2:N-1) # there should be x's for which r1<x1<a, so minimum for a should be 2, and thus minimum for n1 should be 2
#------------------------------------------------------------------------------------------------------------------------------------------------------------
res1 <- lapply(N_min:N_max, FUN=function(N) cbind(N = N, n1 = (2:(N - 1))))
res1 <- do.call(rbind, res1)
res1 <- data.frame( res1)
names(res1) <- c("N","n1")
# Get for selected N's and n1's: r's (1:N-1)
#--------------------------------------------
res2 <- mapply(N=res1$N,n1=res1$n1,
FUN=function(N, n1) cbind(N=N,n1=n1,r=(1:(N-1))),
SIMPLIFY=FALSE)
res2 <- do.call(rbind, res2)
res2 <- data.frame(res2)
res2$n2 <- res2$N - res2$n1
# Get for selected N's, n1's, r's: a's (max(2,r-n2+2)):n1) Note: for min: - there should be x's for which r1<x1<a, so minimum for a should be 2
# - if r>n2, and knowing that you must have (r+1) successes to reject H0 after stage 2,
# then you must already have >= (r+1)-n2 successes in stage 1 with continuation, so r1 should be minimal
# (r-n2) - because contination happens when N(responses) is r1+1 -
# and with at least one response between r1 and a (for continuation), the minimal value for a is thus (r-n2)+2
# for max: a<=n1 that's logical
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
res3 <- mapply(N = res2$N, n1 = res2$n1,r=res2$r, n2=res2$n2,
FUN = function(N, n1, r, n2) cbind(N = N, n1 = n1, r = r, n2 = n2, a=(max(2,r-n2+2)):n1),
SIMPLIFY = FALSE)
res3 <- do.call(rbind, res3)
res2 <- data.frame(res3)
# Calculate beta and alpha
#-------------------------
### use a lookup table for formula Section 2.2 from paper Mander and Thompson
### b: probability mass
### B: cumulative probability
nmax <- N_max
b_p0 <- lapply(1:nmax, FUN = function(n) dbinom(0:nmax, size = n, prob = p0))
b_pa <- lapply(1:nmax, FUN = function(n) dbinom(0:nmax, size = n, prob = pa))
B_p0 <- lapply(1:nmax, FUN = function(n) pbinom(0:nmax, size = n, prob = p0, lower.tail = TRUE))
B_pa <- lapply(1:nmax, FUN = function(n) pbinom(0:nmax, size = n, prob = pa, lower.tail = TRUE))
res2 <- data.table::setDT(res2)
res2$rowid <- seq_len(nrow(res2))
res3 <- res2[, P_Fleming2st_reject_Ha(n1 = n1, n2 = n2, r = r, a=a, b_p0 = b_p0, B_p0 = B_p0, b_pa = b_pa, B_pa = B_pa), by = list(rowid)]
res3 <- data.table::setDF(res3)
res3$diff_beta <- res3$beta_temp - beta
res4 <- res3[res3$diff_beta <= eps, ]
res4$diff_alpha <- res4$alpha_temp - alpha
res5 <- res4[res4$diff_alpha <= eps, ]
res5[2:7] <- lapply(res5[2:7], as.numeric) # convert integer into numeric
if(nrow(res5) == 0){
stop("No data satisfying the H0/Ha criteria")
}
res5$alpha <- alpha
res5$beta <- beta
res5$PET.p0 <- pbinom(q = res5$r1, size = res5$n1, prob = p0, lower.tail = TRUE) + pbinom(q = res5$a-1, size = res5$n1, prob = p0, lower.tail = FALSE)
res5$PET.pa <- pbinom(q = res5$r1, size = res5$n1, prob = pa, lower.tail = TRUE) + pbinom(q = res5$a-1, size = res5$n1, prob = pa, lower.tail = FALSE)
res5$EN.p0 <- res5$N - ((res5$N - res5$n1) * res5$PET.p0)
res5$EN.pa <- res5$N - ((res5$N - res5$n1) * res5$PET.pa)
res5 <- data.table::setDT(res5)
fullresult<<-res5 # 'Output' full result dataset
#---------------------------#
# Which outcome is optimal? #
#---------------------------#
if (opt_under=="H0"){
res5$EN.p<-res5$EN.p0
res5 <- res5[, N_min := min(N)]
res5 <- res5[, EN.p_min := min(EN.p0)]
res5 <- res5[, EN.p_N_min := min(EN.p0) , by = list(N)]
res5 <- res5[, EN.p_N_n1_min := min(EN.p0) , by = list(N,n1)]
}
if (opt_under=="Ha"){
res5$EN.p<-res5$EN.pa
res5 <- res5[, N_min := min(N)]
res5 <- res5[, EN.p_min := min(EN.pa)]
res5 <- res5[, EN.p_N_min := min(EN.pa) , by = list(N)]
res5 <- res5[, EN.p_N_n1_min := min(EN.pa) , by = list(N,n1)]
}
if (int>0){
res6_int <- res5[EN.p == EN.p_N_n1_min & floor((int-int_window)*N)<=n1 & n1<=ceiling((int+int_window)*N), ]
res6_int <- res6_int[, EN.p_N_min_int := min(EN.p) , by = list(N)]
res6_int <- res6_int[EN.p == EN.p_N_min_int , ]
res6_int$INTERIM=int;
}
res6 <- res5[EN.p == EN.p_N_min , ]
res6$OPT <- res6$MIN <- res6$ADMISS <- c("")
res6$OPT[which(res6$EN.p == res6$EN.p_min)] <- "Optimal"
res6$MIN[which(res6$N == res6$N_min)] <- "Minimax" # Note: if multiple designs that meet the criteria for minimax:choose
# design with minimal expected sample size under H0: "Optimal minimax design"
res6 <- data.table::setDF(res6)
# Get admissible designs
y <- res6[, c("N", "EN.p")]
con.ind <- chull(y)[chull((y)) == cummin(chull((y)))]
res <- res6[res6$N >= min(res6$N[res6$MIN == "Minimax"]) & res6$N <= max(res6$N[res6$OPT == "Optimal"]), ]
res$ADMISS[which((rownames(res) %in% c(con.ind)) & (res$N > res$N_min) & (res$EN.p > res$EN.p_min) & (res$N < res$N[res$OPT == "Optimal"]))] <- "Admissible"
if (int>0){
if (opt_under=="H0"){res$EN_opt <- "EN.p0_optimal"}
if (opt_under=="Ha"){res$EN_opt <- "EN.pa_optimal"}
res6_int<-res6_int[res6_int$N >= min(res6$N[res6$MIN == "Minimax"]) & res6_int$N <= max(res6$N[res6$OPT == "Optimal"]), ]
res<-merge(x = res, y = res6_int, by = c("N","n1","n2","r1","a","r2","alpha_temp","beta_temp","diff_beta","diff_alpha","alpha","beta","PET.p0","EN.p0","PET.pa","EN.pa","N_min","EN.p0_min","EN.p0_N_min","EN.p0_N_n1_min","rowid"), all = TRUE)
res$ADMISS[is.na(res$ADMISS)] <- "" # AFter merging: replace NA's with """
res$MIN [is.na(res$MIN) ] <- ""
res$OPT [is.na(res$OPT) ] <- ""
res$EN_opt[is.na(res$EN_opt)] <- ""
}
# Calculate exact 1-2*alpha confidence interval
res$eff <- paste0(res$r2 + 1, "/", res$N, " (", 100 * round((res$r2 + 1) / res$N, 3), "%)")
if (CI_type=="Koyama"){
CI <- mapply(a_=res$N, b_=res$n1, c_=res$r1, d_=res$a, e_=res$r2 + 1,
FUN = function(a_,b_,c_,d_,e_) getCI_Koyama(N=a_, n1=b_, r1=c_, a=d_, k=e_, alpha=alpha, design="Fleming", precision=4))
res$CI_LL <- 100 * unlist(CI[rownames(CI) == "CI_LL", ])
res$CI_UL <- 100 * unlist(CI[rownames(CI) == "CI_UL", ])
}
else {
CI <- mapply(a = res$r2 + 1, b = res$N, FUN = function(a, b) binom::binom.confint(a,b,conf.level=1-2*alpha,methods=CI_type))
res$CI_LL <- round(100 * unlist(CI[rownames(CI) == "lower", ]),2)
res$CI_UL <- round(100 * unlist(CI[rownames(CI) == "upper", ]),2)
}
res <- data.table::setnames(res,
old = c("alpha", "beta"),
new = c("alpha_param", "beta_param"))
res <- data.table::setnames(res,
old = c("alpha_temp", "beta_temp"),
new = c("alpha", "beta"))
if (int>0){
res <- cbind(design_nr=1:dim(res)[1],
res[, c("r1", "a", "n1", "r2", "n2", "N", "eff", "CI_LL", "CI_UL", "EN.p0", "PET.p0", "EN.pa", "PET.pa", "MIN", "OPT", "ADMISS","EN_opt","INTERIM", "alpha", "beta")],
p0 = p0, pa = pa,
res[, c("alpha_param", "beta_param")])
}
if (int==0){
res <- cbind(design_nr=1:dim(res)[1],
res[, c("r1", "a", "n1", "r2", "n2", "N", "eff", "CI_LL", "CI_UL", "EN.p0", "PET.p0", "EN.pa", "PET.pa", "MIN", "OPT", "ADMISS","alpha", "beta")],
p0 = p0, pa = pa,
res[, c("alpha_param", "beta_param")])
}
res <- data.table::setnames(res,
old = c("CI_LL", "CI_UL"),
new = c(paste0(100 - 2 * 100 * alpha, "%CI_LL"), paste0(100 - 2 * 100 * alpha, "%CI_UL")))
attr(res, "inputs") <- list(p0 = p0, pa = pa, alpha = alpha, beta = beta, eps = eps, N_min = N_min, N_max = N_max)
class(res) <- c("2stage", "simon", "data.frame")
res
}
# Validation vs (Mander, Thompson SG. Contemporary Clinical Trials 2010;31:572–578 : Table 1,2 and 3 (only with stop efficacy)
#--------------------------------------------------------------------------------------------------------------------
# Table 1
# test_1_0 <- data.frame(cbind(p0=0.05,pa=0.25,alpha=c(0.1,0.05,0.05),beta=c(0.1,0.2,0.1)))
# for(i in 1:dim(test_1_0)[1]){
# nmax <- fleming1stage(p0=test_1_0[i,]$p0,pa=test_1_0[i,]$pa,alpha=test_1_0[i,]$alpha,beta=test_1_0[i,]$beta,eps=0)$N+15
# res <- fleming2stage(p0=test_1_0[i,]$p0,pa=test_1_0[i,]$pa,alpha=test_1_0[i,]$alpha,beta=test_1_0[i,]$beta,eps=0 ,N_min=10, N_max=nmax,opt_under="H0")
# res_dt <- cbind(res[res$OPT=="Optimal",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")],res[res$MIN=="Minimax",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")])
# if (i==1) {test1_list <- list(res_dt)}
# if (i!=1) {test1_list[[i]] <- res_dt }
# }
# test_1_H0 <- cbind(test_1_0,data.frame(do.call("rbind",test1_list)))
#
# for(i in 1:dim(test_1_0)[1]){
# nmax <- fleming1stage(p0=test_1_0[i,]$p0,pa=test_1_0[i,]$pa,alpha=test_1_0[i,]$alpha,beta=test_1_0[i,]$beta,eps=0)$N+15
# res <- fleming2stage(p0=test_1_0[i,]$p0,pa=test_1_0[i,]$pa,alpha=test_1_0[i,]$alpha,beta=test_1_0[i,]$beta,eps=0 ,N_min=10, N_max=nmax,opt_under="Ha")
# res_dt <- cbind(res[res$OPT=="Optimal",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")],res[res$MIN=="Minimax",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")])
# if (i==1) {test1_list <- list(res_dt)}
# if (i!=1) {test1_list[[i]] <- res_dt }
# }
# test_1_Ha <- cbind(test_1_0,data.frame(do.call("rbind",test1_list)))
#
# # Table 2
# test_2_0 <- data.frame(cbind(p0=0.1,pa=0.3,alpha=c(0.1,0.05,0.05),beta=c(0.1,0.2,0.1)))
# for(i in 1:dim(test_2_0)[1]){
# nmax <- fleming1stage(p0=test_2_0[i,]$p0,pa=test_2_0[i,]$pa,alpha=test_2_0[i,]$alpha,beta=test_2_0[i,]$beta,eps=0)$N+15
# res <- fleming2stage(p0=test_2_0[i,]$p0,pa=test_2_0[i,]$pa,alpha=test_2_0[i,]$alpha,beta=test_2_0[i,]$beta,eps=0 ,N_min=10, N_max=nmax,opt_under="H0")
# res_dt <- cbind(res[res$OPT=="Optimal",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")],res[res$MIN=="Minimax",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")])
# if (i==1) {test1_list <- list(res_dt)}
# if (i!=1) {test1_list[[i]] <- res_dt }
# }
# test_2_H0 <- cbind(test_2_0,data.frame(do.call("rbind",test1_list)))
#
# for(i in 1:dim(test_2_0)[1]){
# nmax <- fleming1stage(p0=test_2_0[i,]$p0,pa=test_2_0[i,]$pa,alpha=test_2_0[i,]$alpha,beta=test_2_0[i,]$beta,eps=0)$N+15
# res <- fleming2stage(p0=test_2_0[i,]$p0,pa=test_2_0[i,]$pa,alpha=test_2_0[i,]$alpha,beta=test_2_0[i,]$beta,eps=0 ,N_min=10, N_max=nmax,opt_under="Ha")
# res_dt <- cbind(res[res$OPT=="Optimal",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")],res[res$MIN=="Minimax",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")])
# if (i==1) {test1_list <- list(res_dt)}
# if (i!=1) {test1_list[[i]] <- res_dt }
# }
# test_2_Ha <- cbind(test_2_0,data.frame(do.call("rbind",test1_list)))
#
#
# # Table 3
# test_3_0 <- data.frame(cbind(p0=0.3,pa=0.5,alpha=c(0.1,0.05,0.05),beta=c(0.1,0.2,0.1)))
# for(i in 1:dim(test_3_0)[1]){
# nmax <- fleming1stage(p0=test_3_0[i,]$p0,pa=test_3_0[i,]$pa,alpha=test_3_0[i,]$alpha,beta=test_3_0[i,]$beta,eps=0)$N+15
# res <- fleming2stage(p0=test_3_0[i,]$p0,pa=test_3_0[i,]$pa,alpha=test_3_0[i,]$alpha,beta=test_3_0[i,]$beta,eps=0 ,N_min=10, N_max=nmax,opt_under="H0")
# res_dt <- cbind(res[res$OPT=="Optimal",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")],res[res$MIN=="Minimax",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")])
# if (i==1) {test1_list <- list(res_dt)}
# if (i!=1) {test1_list[[i]] <- res_dt }
# }
# test_3_H0 <- cbind(test_3_0,data.frame(do.call("rbind",test1_list)))
#
# for(i in 1:dim(test_3_0)[1]){
# nmax <- fleming1stage(p0=test_3_0[i,]$p0,pa=test_3_0[i,]$pa,alpha=test_3_0[i,]$alpha,beta=test_3_0[i,]$beta,eps=0)$N+15
# res <- fleming2stage(p0=test_3_0[i,]$p0,pa=test_3_0[i,]$pa,alpha=test_3_0[i,]$alpha,beta=test_3_0[i,]$beta,eps=0 ,N_min=10, N_max=nmax,opt_under="Ha")
# res_dt <- cbind(res[res$OPT=="Optimal",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")],res[res$MIN=="Minimax",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")])
# if (i==1) {test1_list <- list(res_dt)}
# if (i!=1) {test1_list[[i]] <- res_dt }
# }
# test_3_Ha <- cbind(test_3_0,data.frame(do.call("rbind",test1_list)))
IDDI-BE/PhIIdesign documentation built on June 5, 2021, 2:03 p.m.
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Defines functions fleming2stage.default fleming2stage P_Fleming2st_reject_Ha
Documented in fleming2stage
#------------------------------------------------------#
# Equation from paper Mander and Thompson: Section 2.2 #
#------------------------------------------------------#
P_Fleming2st_reject_Ha <- function(n1, n2, r, a, b_p0, B_p0, b_pa, B_pa){
## r is the total number of seen cases on both n1 and n2 together
## Calculate alpha/beta for all values up to r
r_1 <- (max(0,r-n2)):(max(0,min(a-2, r-1))) # for min: - there should be x's for which r1<x1<a, so minimum for a should be 2
# - if r>n2, and knowing that you must have (r+1) successes to reject H0 after stage 2,
# then you must already have >= (r+1)-n2 successes in stage 1 with continuation, so r1 should be minimal
# (r-n2), - because contination happens when N(responses) is r1+1 -and with one x1 between r1 and a,
# the minimal value for a is thus (r-n2)+2
# for max: r1<=a-2, as there must be at least one value x1, for which r1<x1<a, so trial can continue to stage 2
# also logical that r1<=r-1, as you need r1+1 to proceed to second stage.
# if r1=r than in second stage X1+X2>r, so second stage not needed anymore
x1 <- r_1 + 1
# calculate eta_temp=P(not rejecting H0) under H0 for each (r1,x1)
#-----------------------------------------------------------------
B_p0_r1 <- B_p0[[n1]][1 + r_1] # P(X1<=r1|n1,p0) so pbinom(r_1 ,n1,p0), indexing with "+1", as B_p0 starts from 0 successes
b_p0_x1 <- b_p0[[n1]][1 + x1] # P(X1=x1|n1,p0) so dbinom(x1 ,n1,p0), indexing with "+1", as b_p0 starts from 0 successes
B_p0_r2 <- B_p0[[n2]][1 + (r-x1)] # P(X2<=r-x1|n2,p0) so pbinom(r-x1,n2,p0), indexing with "+1", as B_p0 starts from 0 successes
eta_temp <- B_p0_r1 + rev(cumsum(rev(b_p0_x1 * B_p0_r2))) # e.g. for r=2: r_1=0: P(X1<=0) + P(X1=1)P(X2<=2-1) + P(X1=2)P(X2<=2-2)
# r_1=1: P(X1<=1) + P(X1=2)P(X2<=2-2)
# calculate beta_temp=P(not rejecting H0) under Ha for each (r1,x1)
#------------------------------------------------------------------
B_pa_r1 <- B_pa[[n1]][1 + r_1]
b_pa_x1 <- b_pa[[n1]][1 + x1]
B_pa_r2 <- B_pa[[n2]][1 + (r-x1)]
beta_temp <- B_pa_r1 + rev(cumsum(rev(b_pa_x1 * B_pa_r2)))
list(N = n1 + n2, n1 = n1, r1 = r_1, a=a, n2 = n2, r2 = r, alpha_temp = 1 - eta_temp, beta_temp = beta_temp)
}
#' @title Fleming 2-stage function
#' @description
#' The primary objective of a phase II clinical trial of a new drug or regimen is to determine whether it has sufficient biological activity
#' against the disease under study to warrant more extensive development. \cr
#' This function calculates the sample size needed in a Fleming 2-stage design which is a
#' two-stage design that is optimal in the sense that the expected sample size is minimized if the
#' regimen has low activity subject to constraints upon the size of the type 1 and type 2 errors. \cr
#' Two-stage designs which minimize the maximum sample size are also determined. \cr
#' This type of design also allows stopping for efficacy
#' @param p0 probability of the uninteresting response (null hypothesis \eqn{H0})
#' @param pa probability of the interesting response (alternative hypothesis Ha)
#' @param alpha Type I error rate \eqn{P(reject H0|H0)}
#' @param beta Type II error rate \eqn{P(reject Ha|Ha)}
#' @param eps tolerance default value = 0.005
#' @param N_min minimum sample size value for grid search
#' @param N_max maximum sample size value for grid search
#' @param int pre-specified interim analysis percentage information
#' @param int_window window around interim analysis percentage (e.g. 0.5 +- 0.025). 0.025 is default value
#' @param opt_under optimality under "H0" or "Ha"
#' @param CI_type "Koyama", see \code{\link{getCI_Koyama}}, or any type for \link[binom]{binom.confint}
#' @return a data.frame with elements
#' \itemize{
#' \item n1: total number of patients in stage1
#' \item n2: total number of patients in stage2
#' \item N: total number of patients=n1+n2
#' \item r1: ("r" stands for "rejection") threshold for "rejecting" Ha: if x1<=r1 --> stop for futility at first stage
#' \item r2: ("r" stands for "rejection") threshold for "rejecting" Ha: if x1+x2<=r --> futility at second stage
#' \item a: ("a" for "acceptance") threshold for "accepting" Ha= stop for efficacy at first stage
#' \item eff: (r2 + 1)/N
#' \item CI_LL: (1-2*alpha) CI lower limit
#' \item CI_UL: (1-2*alpha) CI upper limit
#' \item EN.p0: expected sample size under H0
#' \item PET.p0: probability of terminating the trial for futility at the end of the first stage under H0
#' \item EN.pa: expected sample size under Ha
#' \item PET.pa: probability of terminating the trial for efficacy at the end of the first stage under Ha
#' \item MIN: column indicating if the design is the minimal design
#' \item OPT: column indicating if the setting is the optimal design
#' \item ADMISS: column indicating if the setting is the admissible design
#' \item alpha: the actual alpha value which is smaller than \code{alpha_param + eps}
#' \item beta: the actual beta value where which is smaller than \code{beta_param + eps}
#' \item p0: your provided \code{p0} value
#' \item pa: your provided \code{pa} value
#' \item alpha_param: your provided \code{alpha} value
#' \item beta_param: your provided \code{beta} value
#' }
#' @details
#' if x1<=r1 --> stop futility at first stage \cr
#' if r1<x1<a --> proceed to second dtage \cr
#' if x1>=a --> stop efficacy at first stage \cr
#' if (x1+x2)<=r2 --> futility at second stage \cr
#' if (x1+x2)> r2 --> efficacy at second stage \cr
#' So PET.H0=P_H0(X1<=r1)+P_H0(X1>=a1)
#' @references Mander AP, Thompson SG. Two-stage designs optimal under the alternative hypothesis for phase II cancer clinical trials.
#' Contemporary Clinical Trials 2010;31:572–578
#' Qin F et al. Optimal, minimax and admissible two-stage design for phase II oncology clinical trials. BMC Medical Research Methodology 2020;20:126
#' @export
#' @examples
#' result_H0 <-fleming2stage(p0 = 0.3, pa = 0.5, alpha = 0.1, beta = 0.1, eps = 0, N_min = 3,
#' N_max = 50, opt_under="H0")
#' result_Ha <-fleming2stage(p0 = 0.3, pa = 0.5, alpha = 0.1, beta = 0.1, eps = 0, N_min = 3,
#' N_max = 50, opt_under="Ha")
fleming2stage <- function(p0, pa, alpha, beta, eps = 0, N_min, N_max, int=0, int_window=0.025, opt_under="H0", CI_type="Koyama"){
if(length(p0) > 1 && length(pa) > 1){
results <- mapply(null = p0, alternative = pa, alpha = alpha, beta = beta, eps = eps, N_min = N_min, N_max = N_max, int = int, int_window = int_window, opt_under = opt_under, CI_type=CI_type,
FUN = function(null, alternative, alpha, beta, eps, N_min, N_max, int, int_window){
fleming2stage.default(p0 = null, pa = alternative, alpha = alpha, beta = beta, eps = eps, N_min = N_min, N_max = N_max, int = int, int_window = int_window, opt_under = opt_under, CI_type=CI_type)
}, SIMPLIFY = FALSE)
}else{
results <- fleming2stage.default(p0 = p0, pa = pa, alpha = alpha, beta = beta, eps = eps, N_min = N_min, N_max = N_max, int = int, int_window = int_window, opt_under = opt_under, CI_type=CI_type)
}
}
fleming2stage.default <- function(p0, pa, alpha, beta, eps = 0, N_min, N_max, int=0, int_window=0.025, opt_under="H0", CI_type="Koyama") {
# WARNING MESSAGES
if (pa < p0) {
stop("p0 should be smaller than pa")
}
if (N_min <3 ) {
stop("N_min should be >=3")
}
EN.p0 <- EN.p <- EN.p_N_min <- EN.p_N_n1_min <- EN.p_min <- EN.p_N_min_int <- EN.pa <- MIN <- N <- OPT <- NULL
rowid <- n1 <- n2 <- r <- r2 <- a <- NULL
fullresult<-list()
#----------------------------------------------------------------------------------------------------------#
# Get all possible scenarios for N, n1, r1, r2 and n2 #
# Note that this is the possible range of values: 0 <=r1<n1; r1+1<=r; r<=n1+n2 #
#----------------------------------------------------------------------------------------------------------#
# Get for selected N's all possible n1's (2:N-1) # there should be x's for which r1<x1<a, so minimum for a should be 2, and thus minimum for n1 should be 2
#------------------------------------------------------------------------------------------------------------------------------------------------------------
res1 <- lapply(N_min:N_max, FUN=function(N) cbind(N = N, n1 = (2:(N - 1))))
res1 <- do.call(rbind, res1)
res1 <- data.frame( res1)
names(res1) <- c("N","n1")
# Get for selected N's and n1's: r's (1:N-1)
#--------------------------------------------
res2 <- mapply(N=res1$N,n1=res1$n1,
FUN=function(N, n1) cbind(N=N,n1=n1,r=(1:(N-1))),
SIMPLIFY=FALSE)
res2 <- do.call(rbind, res2)
res2 <- data.frame(res2)
res2$n2 <- res2$N - res2$n1
# Get for selected N's, n1's, r's: a's (max(2,r-n2+2)):n1) Note: for min: - there should be x's for which r1<x1<a, so minimum for a should be 2
# - if r>n2, and knowing that you must have (r+1) successes to reject H0 after stage 2,
# then you must already have >= (r+1)-n2 successes in stage 1 with continuation, so r1 should be minimal
# (r-n2) - because contination happens when N(responses) is r1+1 -
# and with at least one response between r1 and a (for continuation), the minimal value for a is thus (r-n2)+2
# for max: a<=n1 that's logical
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
res3 <- mapply(N = res2$N, n1 = res2$n1,r=res2$r, n2=res2$n2,
FUN = function(N, n1, r, n2) cbind(N = N, n1 = n1, r = r, n2 = n2, a=(max(2,r-n2+2)):n1),
SIMPLIFY = FALSE)
res3 <- do.call(rbind, res3)
res2 <- data.frame(res3)
# Calculate beta and alpha
#-------------------------
### use a lookup table for formula Section 2.2 from paper Mander and Thompson
### b: probability mass
### B: cumulative probability
nmax <- N_max
b_p0 <- lapply(1:nmax, FUN = function(n) dbinom(0:nmax, size = n, prob = p0))
b_pa <- lapply(1:nmax, FUN = function(n) dbinom(0:nmax, size = n, prob = pa))
B_p0 <- lapply(1:nmax, FUN = function(n) pbinom(0:nmax, size = n, prob = p0, lower.tail = TRUE))
B_pa <- lapply(1:nmax, FUN = function(n) pbinom(0:nmax, size = n, prob = pa, lower.tail = TRUE))
res2 <- data.table::setDT(res2)
res2$rowid <- seq_len(nrow(res2))
res3 <- res2[, P_Fleming2st_reject_Ha(n1 = n1, n2 = n2, r = r, a=a, b_p0 = b_p0, B_p0 = B_p0, b_pa = b_pa, B_pa = B_pa), by = list(rowid)]
res3 <- data.table::setDF(res3)
res3$diff_beta <- res3$beta_temp - beta
res4 <- res3[res3$diff_beta <= eps, ]
res4$diff_alpha <- res4$alpha_temp - alpha
res5 <- res4[res4$diff_alpha <= eps, ]
res5[2:7] <- lapply(res5[2:7], as.numeric) # convert integer into numeric
if(nrow(res5) == 0){
stop("No data satisfying the H0/Ha criteria")
}
res5$alpha <- alpha
res5$beta <- beta
res5$PET.p0 <- pbinom(q = res5$r1, size = res5$n1, prob = p0, lower.tail = TRUE) + pbinom(q = res5$a-1, size = res5$n1, prob = p0, lower.tail = FALSE)
res5$PET.pa <- pbinom(q = res5$r1, size = res5$n1, prob = pa, lower.tail = TRUE) + pbinom(q = res5$a-1, size = res5$n1, prob = pa, lower.tail = FALSE)
res5$EN.p0 <- res5$N - ((res5$N - res5$n1) * res5$PET.p0)
res5$EN.pa <- res5$N - ((res5$N - res5$n1) * res5$PET.pa)
res5 <- data.table::setDT(res5)
fullresult<<-res5 # 'Output' full result dataset
#---------------------------#
# Which outcome is optimal? #
#---------------------------#
if (opt_under=="H0"){
res5$EN.p<-res5$EN.p0
res5 <- res5[, N_min := min(N)]
res5 <- res5[, EN.p_min := min(EN.p0)]
res5 <- res5[, EN.p_N_min := min(EN.p0) , by = list(N)]
res5 <- res5[, EN.p_N_n1_min := min(EN.p0) , by = list(N,n1)]
}
if (opt_under=="Ha"){
res5$EN.p<-res5$EN.pa
res5 <- res5[, N_min := min(N)]
res5 <- res5[, EN.p_min := min(EN.pa)]
res5 <- res5[, EN.p_N_min := min(EN.pa) , by = list(N)]
res5 <- res5[, EN.p_N_n1_min := min(EN.pa) , by = list(N,n1)]
}
if (int>0){
res6_int <- res5[EN.p == EN.p_N_n1_min & floor((int-int_window)*N)<=n1 & n1<=ceiling((int+int_window)*N), ]
res6_int <- res6_int[, EN.p_N_min_int := min(EN.p) , by = list(N)]
res6_int <- res6_int[EN.p == EN.p_N_min_int , ]
res6_int$INTERIM=int;
}
res6 <- res5[EN.p == EN.p_N_min , ]
res6$OPT <- res6$MIN <- res6$ADMISS <- c("")
res6$OPT[which(res6$EN.p == res6$EN.p_min)] <- "Optimal"
res6$MIN[which(res6$N == res6$N_min)] <- "Minimax" # Note: if multiple designs that meet the criteria for minimax:choose
# design with minimal expected sample size under H0: "Optimal minimax design"
res6 <- data.table::setDF(res6)
# Get admissible designs
y <- res6[, c("N", "EN.p")]
con.ind <- chull(y)[chull((y)) == cummin(chull((y)))]
res <- res6[res6$N >= min(res6$N[res6$MIN == "Minimax"]) & res6$N <= max(res6$N[res6$OPT == "Optimal"]), ]
res$ADMISS[which((rownames(res) %in% c(con.ind)) & (res$N > res$N_min) & (res$EN.p > res$EN.p_min) & (res$N < res$N[res$OPT == "Optimal"]))] <- "Admissible"
if (int>0){
if (opt_under=="H0"){res$EN_opt <- "EN.p0_optimal"}
if (opt_under=="Ha"){res$EN_opt <- "EN.pa_optimal"}
res6_int<-res6_int[res6_int$N >= min(res6$N[res6$MIN == "Minimax"]) & res6_int$N <= max(res6$N[res6$OPT == "Optimal"]), ]
res<-merge(x = res, y = res6_int, by = c("N","n1","n2","r1","a","r2","alpha_temp","beta_temp","diff_beta","diff_alpha","alpha","beta","PET.p0","EN.p0","PET.pa","EN.pa","N_min","EN.p0_min","EN.p0_N_min","EN.p0_N_n1_min","rowid"), all = TRUE)
res$ADMISS[is.na(res$ADMISS)] <- "" # AFter merging: replace NA's with """
res$MIN [is.na(res$MIN) ] <- ""
res$OPT [is.na(res$OPT) ] <- ""
res$EN_opt[is.na(res$EN_opt)] <- ""
}
# Calculate exact 1-2*alpha confidence interval
res$eff <- paste0(res$r2 + 1, "/", res$N, " (", 100 * round((res$r2 + 1) / res$N, 3), "%)")
if (CI_type=="Koyama"){
CI <- mapply(a_=res$N, b_=res$n1, c_=res$r1, d_=res$a, e_=res$r2 + 1,
FUN = function(a_,b_,c_,d_,e_) getCI_Koyama(N=a_, n1=b_, r1=c_, a=d_, k=e_, alpha=alpha, design="Fleming", precision=4))
res$CI_LL <- 100 * unlist(CI[rownames(CI) == "CI_LL", ])
res$CI_UL <- 100 * unlist(CI[rownames(CI) == "CI_UL", ])
}
else {
CI <- mapply(a = res$r2 + 1, b = res$N, FUN = function(a, b) binom::binom.confint(a,b,conf.level=1-2*alpha,methods=CI_type))
res$CI_LL <- round(100 * unlist(CI[rownames(CI) == "lower", ]),2)
res$CI_UL <- round(100 * unlist(CI[rownames(CI) == "upper", ]),2)
}
res <- data.table::setnames(res,
old = c("alpha", "beta"),
new = c("alpha_param", "beta_param"))
res <- data.table::setnames(res,
old = c("alpha_temp", "beta_temp"),
new = c("alpha", "beta"))
if (int>0){
res <- cbind(design_nr=1:dim(res)[1],
res[, c("r1", "a", "n1", "r2", "n2", "N", "eff", "CI_LL", "CI_UL", "EN.p0", "PET.p0", "EN.pa", "PET.pa", "MIN", "OPT", "ADMISS","EN_opt","INTERIM", "alpha", "beta")],
p0 = p0, pa = pa,
res[, c("alpha_param", "beta_param")])
}
if (int==0){
res <- cbind(design_nr=1:dim(res)[1],
res[, c("r1", "a", "n1", "r2", "n2", "N", "eff", "CI_LL", "CI_UL", "EN.p0", "PET.p0", "EN.pa", "PET.pa", "MIN", "OPT", "ADMISS","alpha", "beta")],
p0 = p0, pa = pa,
res[, c("alpha_param", "beta_param")])
}
res <- data.table::setnames(res,
old = c("CI_LL", "CI_UL"),
new = c(paste0(100 - 2 * 100 * alpha, "%CI_LL"), paste0(100 - 2 * 100 * alpha, "%CI_UL")))
attr(res, "inputs") <- list(p0 = p0, pa = pa, alpha = alpha, beta = beta, eps = eps, N_min = N_min, N_max = N_max)
class(res) <- c("2stage", "simon", "data.frame")
res
}
# Validation vs (Mander, Thompson SG. Contemporary Clinical Trials 2010;31:572–578 : Table 1,2 and 3 (only with stop efficacy)
#--------------------------------------------------------------------------------------------------------------------
# Table 1
# test_1_0 <- data.frame(cbind(p0=0.05,pa=0.25,alpha=c(0.1,0.05,0.05),beta=c(0.1,0.2,0.1)))
# for(i in 1:dim(test_1_0)[1]){
# nmax <- fleming1stage(p0=test_1_0[i,]$p0,pa=test_1_0[i,]$pa,alpha=test_1_0[i,]$alpha,beta=test_1_0[i,]$beta,eps=0)$N+15
# res <- fleming2stage(p0=test_1_0[i,]$p0,pa=test_1_0[i,]$pa,alpha=test_1_0[i,]$alpha,beta=test_1_0[i,]$beta,eps=0 ,N_min=10, N_max=nmax,opt_under="H0")
# res_dt <- cbind(res[res$OPT=="Optimal",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")],res[res$MIN=="Minimax",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")])
# if (i==1) {test1_list <- list(res_dt)}
# if (i!=1) {test1_list[[i]] <- res_dt }
# }
# test_1_H0 <- cbind(test_1_0,data.frame(do.call("rbind",test1_list)))
#
# for(i in 1:dim(test_1_0)[1]){
# nmax <- fleming1stage(p0=test_1_0[i,]$p0,pa=test_1_0[i,]$pa,alpha=test_1_0[i,]$alpha,beta=test_1_0[i,]$beta,eps=0)$N+15
# res <- fleming2stage(p0=test_1_0[i,]$p0,pa=test_1_0[i,]$pa,alpha=test_1_0[i,]$alpha,beta=test_1_0[i,]$beta,eps=0 ,N_min=10, N_max=nmax,opt_under="Ha")
# res_dt <- cbind(res[res$OPT=="Optimal",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")],res[res$MIN=="Minimax",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")])
# if (i==1) {test1_list <- list(res_dt)}
# if (i!=1) {test1_list[[i]] <- res_dt }
# }
# test_1_Ha <- cbind(test_1_0,data.frame(do.call("rbind",test1_list)))
#
# # Table 2
# test_2_0 <- data.frame(cbind(p0=0.1,pa=0.3,alpha=c(0.1,0.05,0.05),beta=c(0.1,0.2,0.1)))
# for(i in 1:dim(test_2_0)[1]){
# nmax <- fleming1stage(p0=test_2_0[i,]$p0,pa=test_2_0[i,]$pa,alpha=test_2_0[i,]$alpha,beta=test_2_0[i,]$beta,eps=0)$N+15
# res <- fleming2stage(p0=test_2_0[i,]$p0,pa=test_2_0[i,]$pa,alpha=test_2_0[i,]$alpha,beta=test_2_0[i,]$beta,eps=0 ,N_min=10, N_max=nmax,opt_under="H0")
# res_dt <- cbind(res[res$OPT=="Optimal",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")],res[res$MIN=="Minimax",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")])
# if (i==1) {test1_list <- list(res_dt)}
# if (i!=1) {test1_list[[i]] <- res_dt }
# }
# test_2_H0 <- cbind(test_2_0,data.frame(do.call("rbind",test1_list)))
#
# for(i in 1:dim(test_2_0)[1]){
# nmax <- fleming1stage(p0=test_2_0[i,]$p0,pa=test_2_0[i,]$pa,alpha=test_2_0[i,]$alpha,beta=test_2_0[i,]$beta,eps=0)$N+15
# res <- fleming2stage(p0=test_2_0[i,]$p0,pa=test_2_0[i,]$pa,alpha=test_2_0[i,]$alpha,beta=test_2_0[i,]$beta,eps=0 ,N_min=10, N_max=nmax,opt_under="Ha")
# res_dt <- cbind(res[res$OPT=="Optimal",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")],res[res$MIN=="Minimax",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")])
# if (i==1) {test1_list <- list(res_dt)}
# if (i!=1) {test1_list[[i]] <- res_dt }
# }
# test_2_Ha <- cbind(test_2_0,data.frame(do.call("rbind",test1_list)))
#
#
# # Table 3
# test_3_0 <- data.frame(cbind(p0=0.3,pa=0.5,alpha=c(0.1,0.05,0.05),beta=c(0.1,0.2,0.1)))
# for(i in 1:dim(test_3_0)[1]){
# nmax <- fleming1stage(p0=test_3_0[i,]$p0,pa=test_3_0[i,]$pa,alpha=test_3_0[i,]$alpha,beta=test_3_0[i,]$beta,eps=0)$N+15
# res <- fleming2stage(p0=test_3_0[i,]$p0,pa=test_3_0[i,]$pa,alpha=test_3_0[i,]$alpha,beta=test_3_0[i,]$beta,eps=0 ,N_min=10, N_max=nmax,opt_under="H0")
# res_dt <- cbind(res[res$OPT=="Optimal",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")],res[res$MIN=="Minimax",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")])
# if (i==1) {test1_list <- list(res_dt)}
# if (i!=1) {test1_list[[i]] <- res_dt }
# }
# test_3_H0 <- cbind(test_3_0,data.frame(do.call("rbind",test1_list)))
#
# for(i in 1:dim(test_3_0)[1]){
# nmax <- fleming1stage(p0=test_3_0[i,]$p0,pa=test_3_0[i,]$pa,alpha=test_3_0[i,]$alpha,beta=test_3_0[i,]$beta,eps=0)$N+15
# res <- fleming2stage(p0=test_3_0[i,]$p0,pa=test_3_0[i,]$pa,alpha=test_3_0[i,]$alpha,beta=test_3_0[i,]$beta,eps=0 ,N_min=10, N_max=nmax,opt_under="Ha")
# res_dt <- cbind(res[res$OPT=="Optimal",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")],res[res$MIN=="Minimax",c("r1","a","n1","r2","N","EN.p0","EN.pa","PET.p0","PET.pa")])
# if (i==1) {test1_list <- list(res_dt)}
# if (i!=1) {test1_list[[i]] <- res_dt }
# }
# test_3_Ha <- cbind(test_3_0,data.frame(do.call("rbind",test1_list)))
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