Nothing
##' Find optimal design(s) for a two-period K+M experimental arm platform trial given a
##' user-specified family-wise error rate (or pair-wise error rate) and marginal power. The
##' K+M-experimental arm trial has K experimental arms and one control arm during the first period, and later M
##' experimental arms are added on the start of the second period. The one common control arm is
##' shared among all experimental arms across the trial. The function calculates required sample
##' sizes for each of the experimental arm (n2), the concurrent control (n0_2), the total sample
##' size (N2), the allocation ratios (A1 & A2), and the critical value (z_alpha1) for each
##' experimental arm-control comparison in the trial. The number of patients saved in a K+M-experimental arm
##' trial compared to conducting one K-experimental arm and one M-experimental arm trial separately is also provided. Users
##' can choose to control for either FWER or PWER in the trial.
##'
##' Providing an optimized design in terms of minimizing the total sample size for adding M
##' additional experimental arms in the middle of a clinical trial which originally
##' have K experimental arms and 1 control arm, given user-defined FWER (or PWER) and marginal
##' power. The optimal design for the K+M-experimental arm trial exists only if flag.dpmp = 0. It means that
##' the optimal design can be found to keep both marginal and disjunctive power levels no less
##' than those in the corresponding K-experimental arm trial. If flag.dpmp = 1 and flag.mp = 1, it means the
##' optimal design to maintain both mariginal and disjunctive power levels can not be found, but
##' the a design with the disjunctive power no less than its counterpart in the K-experimental arm trial is
##' returned in \bold{designs}.
##'
##' @title Design an optimal two-period multiarm platform trial with new experimental arms added
##' during the trial, controlling for FWER or PWER
##'
##' @param nt the number of patients already enrolled on each of the K initial experimental arms
##' at the time the M new arms are added.
##' @param K the number of experimental arms in the first period in a two-period K+M-experimental arm trial
##' @param M the number of new experimental arms added at the start of the second period
##' @param fwer the family-wise type I error rate, default to be null, users need to choose
##' between controlling for fwer or pwer and input a value for this argument if fwer is chosen
##' @param pwer the pair-wise type I error rate, default to be null, users need to choose between
##' controlling for fwer or pwer and input a value for this argument if pwer is chosen
##' @param marginal.power the marginal power for each experimental-control comparison in the
##' K-experimental arm trial. This is also the marginal power the algorithm aims to achieve
##' in the K+M-experimental arm when min.marginal.power=marginal.power (default option).
##' @param min.marginal.power the marginal power the function aims to achieve in the K+M-experimental
##' arm trial, default to be the same as the marginal power of the K-experimental arm trial.
##' It will be the marginal power of the K+M-experimental arm if optimal design exists.
##' Don't change the default unless you need to achieve a marginal power level different than
##' that of the K-experimental arm trial.
##' @param delta the standardized effect size expected to be detected in the trial
##' @param seed an integer used in random number generation for numerically evaluating
##' integration, default = 123
##'
##' @return The function returns a list, including \bold{design_Karm}, \bold{designs},
##' \bold{flag.dp}, \bold{flag.mp}, and \bold{flag.dpmp}.
##'
##' @return \bold{design_Karm} contains the design parameters for the K-experimental arm trial including:
##' @return \verb{ }\emph{K}, the number of experimental arms
##' @return \verb{ }\emph{n1}, the sample size for each of the K experimental
##' arms
##' @return \verb{ }\emph{n0_1}, the sample size of the common control arm
##' @return \verb{ }\emph{N1} the total sample size of a K-experimental arm trial
##' @return \verb{ }\emph{z_alpha1}, the critical value for the comparison between any of the K
##' experimental arms and the control
##' @return \verb{ }\emph{FWER1}, the family-wise error rate
##' @return \verb{ }\emph{z_beta1}, the quantile of the marginal power, i.e., qnorm(marginal
##' power)
##' @return \verb{ }\emph{Power1}, the disjunctive power
##' @return \verb{ }\emph{cor0}, the correlation of Z-test statistics between any two of the K
##' experimental arms
##' @return \verb{ }\emph{delta}, the standardized effect size expected to be detected in the
##' K-experimental arm trial
##'
##' @return \bold{designs} contains the recommended optimal design parameters for the K+M-experimental arm
##' trial including:
##' @return \verb{ }\emph{n2} and \emph{n0_2}, the sample sizes of each of the K+M experimental
##' arms and its corresponding concurrent control, respectively
##' @return \verb{ }\emph{nt} and \emph{n0t}, the number of patients already enrolled on each
##' of the K initial experimental arms and the control arm, respectively, at the time the M new
##' arms are added
##' @return \verb{ }\emph{nc}, the total sample size of the control arm for the k+M trial, i.e.
##' , the sum of the concurrent (n0_2) and nonconcurrent (n0t) controls
##' @return \verb{ }\emph{N2}, the total sample size of the two-period K+M-experimental arm trial
##' @return \verb{ }\emph{A1}, the allocation ratio (control to experimental arm) before the
##' M new experimental arms are added and after the initial K experimental arms end
##' @return \verb{ }\emph{A2}, the allocation ratio (control to experimental arm) after the M
##' new experimental arms are added and before the initial K experimental arms end
##' @return \verb{ }\emph{cor1}, the correlation of Z-test statistics between any two of the K
##' initial experimental arms (or between any two of the M new arms)
##' @return \verb{ }\emph{cor2}, the correlation of Z-test statistics between any pair of one
##' initially opened and one newly added experimental arm
##' @return \verb{ }\emph{critical_value2}, the critical value for the comparison between each
##' experimental arm and the concurrent control in the K+M-experimental arm trial
##' @return \verb{ }\emph{mariginal.power2}, the marginal power for the K+M-experimental arm trial
##' @return \verb{ }\emph{disjunctive.power2}, the disjunctive power for the K+M-experimental arm trial
##' @return \verb{ }\emph{FWER2}, the family-wise type-I error rate for the K+M-experimental arm trial
##' @return \verb{ }\emph{delta}, the standardized effect size expected to be detected in the
##' K+M-experimental arm trial
##' @return \verb{ }\emph{save}, the number of patients saved in the K+M-experimental arm trial compared to
##' conducting one K-experimental arm and one M-experimental arm trial separately.
##'
##'@return \bold{flag.dp}, \bold{flag.mp}, and \bold{flag.dpmp} indicate if the lower limit of
##' disjunctive power, marginal power, or both of them has(have) met, respectively
##'
##' @import mvtnorm
##' @export
##'
##' @author \verb{ }Xiaomeng Yuan, Haitao Pan
##'
##' @references \verb{ }Pan, H., Yuan, X. and Ye, J. (2022). An optimal two-period multiarm
##' platform design with new experimental arms added during the trial. Manuscript submitted for
##' publication.
##' @references \verb{ }Dunnett, C. W. (1955). A multiple comparison procedure for comparing
##' several treatments with a control. Journal of the American Statistical Association, 50(272),
##' 1096-1121.
##'
##' @examples
##' \donttest{platform_design(nt = 30, K = 2, M = 2, fwer = 0.025, marginal.power = 0.8,
##' delta = 0.4)}
##' #flag.dpmp == 0, lower limits of marginal and disjunctive power are both met
##' #
##' #$design_Karm
##' # K n1 n0_1 N1 z_alpha1 FWER1 z_beta1 Power1 cor0 delta
##' # 1 2 101 143 345 2.220604 0.025 0.8416212 0.9222971 0.4142136 0.4
##' #
##' #$designs
##' # n2 n0_2 nt n0t nc N2
##' #15669 107 198 30 43 241 669
##' #15994 106 202 30 43 245 669
##' #16315 105 206 30 43 249 669
##' #16632 104 210 30 43 253 669
##' #
##' # A1 A2 cor1 cor2 critical_value2
##' #15669 1.414214 2.012987 0.3508197 0.2746316 2.475233
##' #15994 1.414214 2.092105 0.3441558 0.2708949 2.475790
##' #16315 1.414214 2.173333 0.3376206 0.2671464 2.476330
##' #16632 1.414214 2.256757 0.3312102 0.2633910 2.476854
##' #
##' # marginal.power2 disjunctive.power2
##' #15669 0.8001100 0.9853799
##' #15994 0.8003363 0.9857541
##' #16315 0.8003878 0.9860900
##' #16632 0.8002699 0.9863903
##' #
##' # FWER2 delta save
##' #15669 0.025 0.4 21
##' #15994 0.025 0.4 21
##' #16315 0.025 0.4 21
##' #16632 0.025 0.4 21
##' #
##' #$flag.dp
##' #[1] 0
##' #
##' #$flag.mp
##' #[1] 0
##' #
##' #$flag.dpmp
##' #[1] 0
platform_design <- function(nt, K, M, fwer=NULL, pwer=NULL, marginal.power,
min.marginal.power = marginal.power, delta,
seed=123) {
if (sum(is.null(fwer)+is.null(pwer))!=1)
stop("Users need to decide if controlling for fwer or pwer.
Please input a value for either fwer or pwer.")
# control fwer
if(is.null(fwer)==0){
if (sum(fwer <= 0, fwer >= 1) == 1)
stop("0 < fwer < 1 not true")}
# control pwer
if(is.null(pwer)==0){
if (sum(pwer <= 0, pwer >= 1) == 1)
stop("0 < pwer < 1 not true")}
if (sum(c(nt, K, M)%%1) != 0)
stop("nt, K and M should all be integers.")
if (sum(c(nt, K, M) > 0) != length(c(nt, K, M)))
stop("nt, K, and M should all > 0.")
if (sum(marginal.power <= 0, marginal.power >= 1) == 1)
stop("0 < mariginal.power < 1 not true")
if (sum(min.marginal.power <= 0, min.marginal.power >= 1) == 1)
stop("0 < min.mariginal.power < 1 not true")
flag.dp <- flag.mp <- flag.dpmp <- 0
ntrt <- K + M
A1 <- sqrt(K)
n0t <- ceiling(A1 * nt)
# original design parameters, control for fwer or pwer
if(is.null(fwer)==0){multi <- one_stage_multiarm(K=K, fwer=fwer, marginal.power=marginal.power,
delta=delta, seed=seed)}
else {multi <- one_stage_multiarm(K=K, pwer=pwer, marginal.power=marginal.power,
delta=delta, seed=seed)}
n1 <- multi$n1
n0_1 <- multi$n0_1
if (nt >= n1)
stop("nt has to be smaller than n1")
if (n0t >= n0_1)
stop("n0t has to be smaller than n0_1")
N1 <- multi$N1
corMat1 <- multi$corMat1
z_beta1 <- multi$z_beta1
Power1 <- multi$Power1
min.disjunctive.power <- multi$Power1
z_alpha1 <- multi$z_alpha1
FWER1 <- multi$FWER1
design_Karm <- data.frame(K=multi$K,
n1=multi$n1, n0_1=multi$n0_1,N1=multi$N1,
z_alpha1=multi$z_alpha1,FWER1=multi$FWER1,
z_beta1=multi$z_beta1, Power1=multi$Power1,
cor0=1/(A1 + 1),
delta=multi$delta)
# Upper limit of total sample size S
if (K == M) {
S <- 2 * N1
} else {
if(is.null(fwer)==0){
multi_m <- one_stage_multiarm(K=M, fwer=fwer, marginal.power=marginal.power,
delta=delta, seed=seed)
}
else{multi_m <- one_stage_multiarm(K=M, pwer=pwer, marginal.power=marginal.power,
delta=delta, seed=seed)}
S <- multi$N1 +multi_m$N1
}
# admissible set
pair <- admiss(n1, n0_1, nt, ntrt, S)
# update design parameters
Vone_design2 <- Vectorize(one_design2, vectorize.args = c("n2", "n0_2"))
dat <- Vone_design2(K = K, M = M,
n1 = n1, n0_1 = n0_1,
n2 = pair$Var1, n0_2 = pair$Var2,
nt = nt, n0t = n0t,
fwer = fwer,
z_alpha1 = z_alpha1, z_beta1 = z_beta1,
seed=seed)
dat.u <- unlist(dat)
dat.d <- matrix(dat.u, nrow = 12, byrow = F)
dat.f <- data.frame(n2 = dat.d[1, ], n0_2 = dat.d[2, ],
nt = dat.d[3, ], n0t = dat.d[4,],
nc = dat.d[5, ], N2 = dat.d[6, ],
A1 = A1, A2 = dat.d[7, ],
cor1 = dat.d[8, ], cor2 = dat.d[9,],
critical_value2 = dat.d[10, ],
marginal.power2 = dat.d[11,],
disjunctive.power2 = dat.d[12, ])
# Select recommended designs
dat.dp <- dat.f[dat.f$disjunctive.power2 >= min.disjunctive.power, ]
if (nrow(dat.dp) == 0) {
flag.dp <- 1
}
dat.mp <- dat.f[dat.f$marginal.power2 >= min.marginal.power, ]
if (nrow(dat.mp) == 0) {
flag.mp <- 1
}
## designs meets both limits
dat.dpmp <- dat.f[dat.f$disjunctive.power2 >= min.disjunctive.power & dat.f$marginal.power2 >= min.marginal.power,
]
if (nrow(dat.dpmp) == 0) {
flag.dpmp <- 1
}
if (flag.dpmp == 0) {
dats <- dat.dpmp
message("flag.dpmp == 0, lower limits of marginal and disjunctive power are both met")
} else if (flag.dpmp == 1 & flag.dp == 0) {
dats <- dat.dp
warning("flag.mp == 1, lower limit of the marginal power can not be met, recommended design(s) selected from designs with disjunctive power >= Power1")
} else if (flag.dpmp == 1 & flag.mp == 0) {
dats <- dat.mp
warning(" flag.dp == 1, lower limit of the disjunctive power can not be met, recommended design(s) selected from designs with marginal power >= min.marginal.power")
} else {
dats <- NULL
warning("The lower limit of neither marginal nor disjuctive power is met, please redefine nt or min.marginal.power.")
}
## Select designs with minimum sample size
designs <- dats[dats$N2 == min(dats$N2), ]
## if control pwer, calculate fwer for optimal designs K+M trial
if(is.null(pwer)==0){
FWER2 <- rep(0, nrow(designs))
for (i in 1:nrow(designs)){
corMat2 <- cor.mat(K, M, n=designs$n2[i], n0=designs$n0_2[i], n0t=designs$n0t[i])$cormat
FWER2[i] <- 1 - pmvnorm(lower = rep(-Inf, ntrt), upper = rep(z_alpha1, ntrt),
mean = rep(0, ntrt), corr = corMat2)
}
designs$FWER2 <- FWER2
}
## if control fwer, report fwer for optimal designs K+M trial
if(is.null(fwer)==0){
designs$FWER2 <- fwer
}
designs$delta <- delta
designs$save <- S-designs$N2
res <- list(design_Karm = design_Karm,
designs = designs,
flag.dp = flag.dp,
flag.mp = flag.mp,
flag.dpmp = flag.dpmp)
return(res)
}
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