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DesignClassGroupSequential2Subpopulations1TreatmentVsControl.R
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DesignClassGroupSequential2Subpopulations1TreatmentVsControl.R
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# This class of group sequential designs is based on the file
# DesignClass2Subpopulations1TreatmentVsControl.R
# The main change is that the only allowed adaptations are to stop all accrual
# or continue all accrual.
# The multiple testing procedure (for efficacy) is identical to that in the adaptive designs DesignClass2Subpopulations1TreatmentVsControl.R).
# The stopping rule is as follows:
# All accrual stops at the first interim analysis k where
# at least one of (a) and (b) holds for each subpopulation s:
# a. H0s has been rejected at the current or previous analysis
# b. the futility boundary is crossed (Z_s<futilty boundary)
#
# Uses library(mvtnorm)
# Comparing a single treatment to control in two disjoint sub-populations
# This R file creates the necessary backend files for the optimizer to call
# There are three key functions
# 1) construct.joint.distribution.of.test.statistics.GroupSequential.OneTreatmentArm creates mean and covariance
# matrices associated with the vector of statistics
# 2) get.eff.bound calculates the efficacy boundaries for the design
# 3) design.evaluate for different vectors of test statistics calculates
# which hypothesis are rejected and at which stage.
# Throughout the sequence of test statistics, is given by blocks of stages and within a block
# of a stage k the vector of test statistics is given by
# (Z_{1,k}, Z_{2,k}), where the first subscript
# indicates treatment and the second stage.
# This function calculates the covariate matrix for a binary and a continuous outcome
# It assumes one treatments and
# a control, two sub-populations, and arbitrary number of stages
# prop.samp.vec.pop.1
# Inputs: var.vec.pop.1: variance vector for population 1
# var.vec.pop.2: variance vector for population 2
# prop.samp.vec.pop.1: the proportion of total number of subjects in sub-population one
# which are enrolled at each stage. E.g. c(0.5, 0.5) means 50% of the obs are enrolled
# at stage one and 50% at stage 2.
# prop.samp.vec.pop.1: the proportion of total number of subjects in sub-population one
# which are enrolled at each stage. E.g. c(0.5, 0.5) means 50 \% of the obs are enrolled
# at stage one and 50% at stage 2.
# Output: The covariance matrix associated with the vector of test statistics
cov.mat.cont.bin.GroupSequential.OneTreatmentArm = function(var.vec.pop.1, var.vec.pop.2,
prop.samp.vec.pop.1, prop.samp.vec.pop.2){
# K is the number of stages
K = length(prop.samp.vec.pop.1)
# Proportion of sample size enrolled up to and until stage k in each population
cumsum.prop.samp.pop.1 = cumsum(prop.samp.vec.pop.1)
cumsum.prop.samp.pop.2 = cumsum(prop.samp.vec.pop.2)
cov.mat = matrix(0, nrow = 2 * K, ncol = 2 * K)
# Filling in the covariance matrix
# Filling in by blocks
for(i in 1:K){
for(j in 1:K){
min.max.term.pop.1 =
sqrt(min(cumsum.prop.samp.pop.1[i], cumsum.prop.samp.pop.1[j])/
max(cumsum.prop.samp.pop.1[i], cumsum.prop.samp.pop.1[j]))
min.max.term.pop.2 =
sqrt(min(cumsum.prop.samp.pop.2[i], cumsum.prop.samp.pop.2[j])/
max(cumsum.prop.samp.pop.2[i], cumsum.prop.samp.pop.2[j]))
# (Z_{1,1}, Z_{2,1}, Z_{1,2}, Z_{2,2})
cov.mat[(i-1)*2 + 1, (j-1)*2 + 1] = min.max.term.pop.1
cov.mat[(i-1)*2 + 2, (j-1)*2 + 2] = min.max.term.pop.2
# cov.mat[(i-1)*2 + 3, (j-1)*2 + 3] = min.max.term.pop.1
# cov.mat[(i-1)*2 + 4, (j-1)*2 + 4] = min.max.term.pop.2
}
}
return(cov.mat)
}
# This function calculates the covariance matrix associatec with
# a survival outcome.
# Input d.l.j, l = 0,1, and j = 1,2. Here, d.l.j is a vector of length
# K where element k is the expected number of deaths at or before analysis
# k in subpopulation j and treatment l.
# Output covariance matrix associate with vector of test statistics
cov.mat.surv.GroupSequential.OneTreatmentArm = function(d.0.1, d.1.1, d.0.2, d.1.2){
K = length(d.0.1)
cov.mat = matrix(0, nrow = 2 * K, ncol = 2 * K)
# Filling in by blocks
for(i in 1:K){
for(j in 1:K){
min.max.term.pop.1.treatment.1 = sqrt((d.0.1[min(i,j)] + d.1.1[min(i,j)])/(d.0.1[max(i,j)] + d.1.1[max(i,j)]))
min.max.term.pop.2.treatment.1 = sqrt((d.0.2[min(i,j)] + d.1.2[min(i,j)])/(d.0.2[max(i,j)] + d.1.2[max(i,j)]))
# Both treatment 1 and subpopulation 1 different stages
cov.mat[(i-1) * 2 + 1, (j-1) * 2 + 1] = min.max.term.pop.1.treatment.1
# Both treatment 1 and subpopulation 2 different stages
cov.mat[(i-1) * 2 + 2, (j-1) * 2 + 2] = min.max.term.pop.2.treatment.1
}
}
# Throughout the sequence of test statistics is given by blocks of stages
# and within a block of a stage k the vector of test statistics is given by
# (Z_{1,1,k}, Z_{1,2,k}, Z_{2,1,k}, Z_{2,2,k}), where the first subscript
# indicates treatment, the second sub-populaiton and the third stage.
return(cov.mat)
}
# This function calculates the covariance matrix associatec with
# a survival outcome when just sub-population one is enrolled at beginning of trial.
# Inputs: d.i.j a vector corresponding to the of expected number of deaths in treatment i
# and subpopulation j at each stage
# Output: Covarince matrix
cov.mat.surv.restrict.yes.GroupSequential.OneTreatmentArm = function(d.0.1, d.1.1, d.0.2, d.1.2){
# Creating covariance matrix (note we delete a row and a column later)
K = length(d.0.1)
cov.mat = matrix(0, nrow = 2 * K , ncol = 2 * K)
# Filling in by blocks for subpopulation 1
for(i in 1:K){
for(j in 1:K){
min.max.term.pop.1.treatment.1 = sqrt((d.0.1[min(i,j)] + d.1.1[min(i,j)])/(d.0.1[max(i,j)] + d.1.1[max(i,j)]))
# Both treatment 1 and subpopulation 1 different stages
cov.mat[(i-1) * 2 + 1, (j-1) * 2 + 1] = min.max.term.pop.1.treatment.1
}
}
# Remove second row and second column as Z_{2,1} not available
cov.mat = cov.mat[, -2]
cov.mat = cov.mat[-2,]
# Filling in by blocks for subpopulation 1
for(i in 1:(K-1)){
for(j in 1:(K-1)){
min.max.term.pop.2.treatment.1 = sqrt((d.0.2[min(i,j)] + d.1.2[min(i,j)])/(d.0.2[max(i,j)] + d.1.2[max(i,j)]))
# Both treatment 1 and subpopulation 2 different stages
cov.mat[((i-1) * 2 + 3), ((j-1) * 2 + 3)] = min.max.term.pop.2.treatment.1
}
}
return(cov.mat)
}
# This function creates the covariance matrix and mean vector
# associated with the test statistic
# Inputs: # analytic.n.per.stage - [K x J(L+1)] matrix: patients with primary outcome
# at each interim analysis. For a survival outcome this should be total patients enrolled which
#. equals analytic.n.per.stage if dealy is set to zero
# stage 1: T0S1 T0S2 ... T0SJ ... TLS1 TLS2 ... TLSJ
# stage 2: T0S1 T0S2 ... T0SJ ... TLS1 TLS2 ... TLSJ
# ...
# stage k: T0S1 T0S2 ... T0SJ ... TLS1 TLS2 ... TLSJ
# outcome.type: type of outcome, one of continuous, binary or survival
# mean.sub.pop.1: the assumed means assocated with each treatment in sub-population 1
# the mean vector is input in the order (control, treatment) with
# mean.sub.pop.2: the assumed means assocated with each treatment in sub-population 2
# the mean vector is input in the order (control, treatment) with
# var.vec.pop.1: the variance vector associated with each treatment in sub-population one
# var.vec.pop.2: the variance vector associated with each treatment in sub-population two
# prop.pop.1: The proportion of subjects in population one. Assumed known.
# NOTE: For a survival outcome the code is setup such that if the hazard rate in the treatment
# group is smaller than in the control group that leads to large value of the test statistics
# and rejection of the null-hypothesis.
# NI: A logical variable if a non-inferiority test is done
# ni.margin: the non-inferiority margin. Only for a non-inferiority test.
# NOTE: Non-inferiority only works for a survival outcome and the non-inferiority margin
# is \lambda_{Treatment}/\lambda_{control} and is therefore greater than 1.
# max.follow: For survival outcome, how long each participant is followed up for
# enrollment.period: For survival outcome, the maximum time participants are enrolled
# hazard.rate.pop.1: For survival outcome, hazard rate for subpopulation 1 in
# the order (control, treatment)
# hazard.rate.pop.2: For survival outcome, hazard rate for subpopulation 2 in
# the order (control, treatment)
# time: for survival outcomes is the timing of all analysis, only needs to be specified for
# a survival outcome
# restrict.enrollment: for a survival outcome, if enrollment at the start data of the trial
# is restricted only to sub-population 1
# censoring.rate: For a survival outcome only. It is the proportion of participants that are not
# administratively censored which drop out of the study. For example, if 100 events are expected
# without any dropout then setting censoring rate to 0.5 means that 100*0.5 events are expected
# relative.efficiency: ratio of the asymptotic variance of unadjusted estimator to
# asymptotic variance of adjusted estimator. Should be greater than one.
# Output: A list with three elements:
# cov.mat.used: Covariance matrix associated with vector of test statistic.
# non.centrality.parameter.vec = The mean vector associated with each test statistic.
# information.vector = for a given stage k elements [(1+ (k-1) * 2):(2 + (k-1) * 2)] are
# (var(\beta_{1,k}, var(\beta_{2,k})) where the
# first subscript indicates sub-populaiton and the second stage.
# beta is the estimator of the treatment effect
# Connection to Betz et al. paper: "Comparison of Adaptive Randomized Trial Designs for Time-to-Event Outcomes that Expand Versus Restrict Enrollment Criteria, to Test Non-Inferiority Design Optimization"
# The 3 design classes defined in Section 5.1 of that paper are:
# 1. D_{ONE-STAGE} [standard, single stage designs]
# 2. D_{ADAPTIVE,START-BOTH} [adaptive, starts by enrolling both subpops in stage 1, can restrict enrollment at interim analyses]
# 3. D_{ADAPTIVE,START-SUBPOP.1} [adaptive, starts by enrolling only subpop. 1; can initiate enrollment of subpop 2 based on decision at first interim analysis. At interim analyses after the first, the decisions are to continue enrollment or restrict.]
# To get the design class (1), use a single stage and set restrict.enrollment = FALSE.
# To get the design class (2), set restrict.enrollment = FALSE.
# To get the design class (3), set restrict.enrollment = TRUE.
construct.joint.distribution.of.test.statistics.GroupSequential.OneTreatmentArm <- function(analytic.n.per.stage,
mean.sub.pop.1=NULL,
mean.sub.pop.2=NULL,
var.vec.pop.1=NULL,
var.vec.pop.2=NULL,
outcome.type,
prop.pop.1,
NI = FALSE,
ni.margin = NULL,
max.follow = NULL,
enrollment.period = NULL,
hazard.rate.pop.1 = NULL,
hazard.rate.pop.2 = NULL,
time = NULL,
restrict.enrollment = FALSE,
censoring.rate = NULL,
relative.efficiency = NULL){
# Number of stages
K <- nrow(analytic.n.per.stage)
# Calculating the total number of subjects at each analysis for both sub-populations
# Note: We assume that an equal number is enrolled to treatment and control.
n.pop.1 = analytic.n.per.stage[, 1]
n.pop.2 = analytic.n.per.stage[, 2]
# Calculating the proportion of observation sampled at each stage for the
# Two treatments
prop.samp.vec.pop.1 = diff(c(0, n.pop.1))/n.pop.1[K]
prop.samp.vec.pop.2 = diff(c(0, n.pop.2))/n.pop.2[K]
# Creating storage space for mean vector
mean.vec = rep(NA, 2 * K)
# Do the calculations seperately depending on the type of outcome
if(outcome.type == "continuous"){
for(i in 1:K){
mean.vec[((i-1)*2+1):((i-1)*2+2)] =
c(sqrt(n.pop.1[i])*(mean.sub.pop.1[2]-mean.sub.pop.1[1])/
sqrt(var.vec.pop.1[2]+var.vec.pop.1[1]),
sqrt(n.pop.2[i])*(mean.sub.pop.2[2] - mean.sub.pop.2[1])/
sqrt(var.vec.pop.2[2]+var.vec.pop.2[1]))
}
cov.mat.used = cov.mat.cont.bin.GroupSequential.OneTreatmentArm(var.vec.pop.1,
var.vec.pop.2,
prop.samp.vec.pop.1,
prop.samp.vec.pop.2)
}
if(outcome.type == "binary"){
var.vec.pop.1 = mean.sub.pop.1*(1 - mean.sub.pop.1)
var.vec.pop.2 = mean.sub.pop.2*(1 - mean.sub.pop.2)
for(i in 1:K){
mean.vec[((i-1)*2+1):((i-1)*2+2)] =
c(sqrt(n.pop.1[i])*(mean.sub.pop.1[2]-mean.sub.pop.1[1])/
sqrt(var.vec.pop.1[2]+var.vec.pop.1[1]),
sqrt(n.pop.2[i])*(mean.sub.pop.2[2] - mean.sub.pop.2[1])/
sqrt(var.vec.pop.2[2]+var.vec.pop.2[1]))
}
cov.mat.used = cov.mat.cont.bin.GroupSequential.OneTreatmentArm(var.vec.pop.1,
var.vec.pop.2,
prop.samp.vec.pop.1,
prop.samp.vec.pop.2)
}
# Create information vector on the extimator scale
# for a given stage k elements [(1+ (k-1) * 4):(4 + (k-1) * 4)] are
# (var(\beta_{1,1,k}, var(\beta_{1,2,k}), var(\beta_{2,1,k}), var(beta_{2,2,k})
#, where the first subscript indicates treatment, the second sub-populaiton and the third stage.
# For a continuous outcome
if(outcome.type == "continuous"){
# Initialize the vector
information.vector.inv = rep(NA, 2 * K)
for(i in 1:K){
information.vector.inv[((i-1)*2+1):((i-1)*2+2)] =
c(1/n.pop.1[i] * (var.vec.pop.1[2]+var.vec.pop.1[1]),
1/n.pop.2[i] * (var.vec.pop.2[2]+var.vec.pop.2[1]))
}
} # End if outcome.type is continuous
# For a binary outcome
if(outcome.type == "binary"){
# Initialize the vector
information.vector.inv = rep(NA, 2 * K)
var.vec.pop.1 = mean.sub.pop.1*(1 - mean.sub.pop.1)
var.vec.pop.2 = mean.sub.pop.2*(1 - mean.sub.pop.2)
for(i in 1:K){
information.vector.inv[((i-1)*2 + 1):((i-1)*2 + 2)] =
c(1/n.pop.1[i] * (var.vec.pop.1[2]+var.vec.pop.1[1]),
1/n.pop.2[i] * (var.vec.pop.2[2]+var.vec.pop.2[1]))
}
} # End if outcome.type is binary
if(outcome.type != "survival"){
# Make information vector fit Tianchens code
information.vector.inv.matrix = matrix(information.vector.inv, nrow = K, byrow = TRUE)
}
if(outcome.type == "survival"){
mean.sub.pop.1 = 1/hazard.rate.pop.1
mean.sub.pop.2 = 1/hazard.rate.pop.2
# Do modification for NI test
if(NI == FALSE){
# The ratios of log-rank tests
theta = -c(log(hazard.rate.pop.1[2]/hazard.rate.pop.1[1]), log(hazard.rate.pop.2[2]/hazard.rate.pop.2[1]))
}
if(NI == TRUE){
# The ratios of log-rank tests
theta = -c(log(hazard.rate.pop.1[2]/hazard.rate.pop.1[1]), log(hazard.rate.pop.2[2]/hazard.rate.pop.2[1])) + log(ni.margin)
}
if(restrict.enrollment == FALSE){
# non-centraility parameter in same order as test statistics mentioned above
mean.vec = rep(NA, 2 * K)
d.0.1 = rep(0,K) # number of deaths in control group pop 1
d.1.1 = rep(0,K) # number of deaths in treatment group pop 1
d.0.2 = rep(0,K) # number of deaths in control group pop 2
d.1.2 = rep(0,K) # number of deaths in treatment group pop 2
for(i in 1:K){
# Calculating the expected number of deaths for each treatment + sub-population combination
# at interim analys i
# We cycle through the 6 different cases
if(enrollment.period >= time[i] & time[i] >= max.follow){
d.0.1[i] = (time[i] - max.follow)/time[i] * (1 - exp(-hazard.rate.pop.1[1] * max.follow)) + max.follow/time[i] * (1 - (1-exp(-hazard.rate.pop.1[1] * max.follow))/(max.follow * hazard.rate.pop.1[1]))
d.1.1[i] = (time[i] - max.follow)/time[i] * (1 - exp(-hazard.rate.pop.1[2] * max.follow)) + max.follow/time[i] * (1 - (1-exp(-hazard.rate.pop.1[2] * max.follow))/(max.follow * hazard.rate.pop.1[2]))
d.0.2[i] = (time[i] - max.follow)/time[i] * (1 - exp(-hazard.rate.pop.2[1] * max.follow)) + max.follow/time[i] * (1 - (1-exp(-hazard.rate.pop.2[1] * max.follow))/(max.follow * hazard.rate.pop.2[1]))
d.1.2[i] = (time[i] - max.follow)/time[i] * (1 - exp(-hazard.rate.pop.2[2] * max.follow)) + max.follow/time[i] * (1 - (1-exp(-hazard.rate.pop.2[2] * max.follow))/(max.follow * hazard.rate.pop.2[2]))
}
if(enrollment.period >= max.follow & max.follow >= time[i]){
d.0.1[i] = 1 - (1-exp(-hazard.rate.pop.1[1] * time[i]))/(time[i] * hazard.rate.pop.1[1])
d.1.1[i] = 1 - (1-exp(-hazard.rate.pop.1[2] * time[i]))/(time[i] * hazard.rate.pop.1[2])
d.0.2[i] = 1 - (1-exp(-hazard.rate.pop.2[1] * time[i]))/(time[i] * hazard.rate.pop.2[1])
d.1.2[i] = 1 - (1-exp(-hazard.rate.pop.2[2] * time[i]))/(time[i] * hazard.rate.pop.2[2])
}
if(time[i] >= enrollment.period & enrollment.period >= max.follow){
k = time[i] - enrollment.period
d.0.1[i] = (enrollment.period + k -max.follow)/enrollment.period * (1 - exp(-hazard.rate.pop.1[1] * max.follow)) + (max.follow - k)/enrollment.period * (1 - (exp(-hazard.rate.pop.1[1] * k) - exp(-hazard.rate.pop.1[1] * max.follow))/((max.follow - k) * hazard.rate.pop.1[1]))
d.1.1[i] = (enrollment.period + k -max.follow)/enrollment.period * (1 - exp(-hazard.rate.pop.1[2] * max.follow)) + (max.follow - k)/enrollment.period * (1 - (exp(-hazard.rate.pop.1[2] * k) - exp(-hazard.rate.pop.1[2] * max.follow))/((max.follow - k) * hazard.rate.pop.1[2]))
d.0.2[i] = (enrollment.period + k -max.follow)/enrollment.period * (1 - exp(-hazard.rate.pop.2[1] * max.follow)) + (max.follow - k)/enrollment.period * (1 - (exp(-hazard.rate.pop.2[1] * k) - exp(-hazard.rate.pop.2[1] * max.follow))/((max.follow - k) * hazard.rate.pop.2[1]))
d.1.2[i] = (enrollment.period + k -max.follow)/enrollment.period * (1 - exp(-hazard.rate.pop.2[2] * max.follow)) + (max.follow - k)/enrollment.period * (1 - (exp(-hazard.rate.pop.2[2] * k) - exp(-hazard.rate.pop.2[2] * max.follow))/((max.follow - k) * hazard.rate.pop.2[2]))
}
if(max.follow >= enrollment.period & enrollment.period >= time[i]){
d.0.1[i] = 1 - (1-exp(-hazard.rate.pop.1[1] * time[i]))/(time[i] * hazard.rate.pop.1[1])
d.1.1[i] = 1 - (1-exp(-hazard.rate.pop.1[2] * time[i]))/(time[i] * hazard.rate.pop.1[2])
d.0.2[i] = 1 - (1-exp(-hazard.rate.pop.2[1] * time[i]))/(time[i] * hazard.rate.pop.2[1])
d.1.2[i] = 1 - (1-exp(-hazard.rate.pop.2[2] * time[i]))/(time[i] * hazard.rate.pop.2[2])
}
if(max.follow >= time[i] & time[i] >= enrollment.period){
d.0.1[i] = 1 - (exp(-hazard.rate.pop.1[1] * (time[i]-enrollment.period)) - exp(-hazard.rate.pop.1[1] * time[i]))/(enrollment.period * hazard.rate.pop.1[1])
d.1.1[i] = 1 - (exp(-hazard.rate.pop.1[2] * (time[i]-enrollment.period)) - exp(-hazard.rate.pop.1[2] * time[i]))/(enrollment.period * hazard.rate.pop.1[2])
d.0.2[i] = 1 - (exp(-hazard.rate.pop.2[1] * (time[i]-enrollment.period)) - exp(-hazard.rate.pop.2[1] * time[i]))/(enrollment.period * hazard.rate.pop.2[1])
d.1.2[i] = 1 - (exp(-hazard.rate.pop.2[2] * (time[i]-enrollment.period)) - exp(-hazard.rate.pop.2[2] * time[i]))/(enrollment.period * hazard.rate.pop.2[2])
}
if(time[i] >= max.follow & max.follow >= enrollment.period){
d.0.1[i] = (time[i] - max.follow)/enrollment.period * (1 - exp(-hazard.rate.pop.1[1] * max.follow)) + (enrollment.period - time[i] + max.follow)/enrollment.period * (1 - (exp(-hazard.rate.pop.1[1] * (time[i]-enrollment.period)) - exp(-hazard.rate.pop.1[1] * max.follow))/((max.follow - time[i] + enrollment.period) * hazard.rate.pop.1[1]))
d.1.1[i] = (time[i] - max.follow)/enrollment.period * (1 - exp(-hazard.rate.pop.1[2] * max.follow)) + (enrollment.period - time[i] + max.follow)/enrollment.period * (1 - (exp(-hazard.rate.pop.1[2] * (time[i]-enrollment.period)) - exp(-hazard.rate.pop.1[2] * max.follow))/((max.follow - time[i] + enrollment.period) * hazard.rate.pop.1[2]))
d.0.2[i] = (time[i] - max.follow)/enrollment.period * (1 - exp(-hazard.rate.pop.2[1] * max.follow)) + (enrollment.period - time[i] + max.follow)/enrollment.period * (1 - (exp(-hazard.rate.pop.2[1] * (time[i]-enrollment.period)) - exp(-hazard.rate.pop.2[1] * max.follow))/((max.follow - time[i] + enrollment.period) * hazard.rate.pop.2[1]))
d.1.2[i] = (time[i] - max.follow)/enrollment.period * (1 - exp(-hazard.rate.pop.2[2] * max.follow)) + (enrollment.period - time[i] + max.follow)/enrollment.period * (1 - (exp(-hazard.rate.pop.2[2] * (time[i]-enrollment.period)) - exp(-hazard.rate.pop.2[2] * max.follow))/((max.follow - time[i] + enrollment.period) * hazard.rate.pop.2[2]))
}
d.0.1[i] = d.0.1[i] * n.pop.1[i] * (1 - censoring.rate)
d.1.1[i] = d.1.1[i] * n.pop.1[i] * (1 - censoring.rate)
d.0.2[i] = d.0.2[i] * n.pop.2[i] * (1 - censoring.rate)
d.1.2[i] = d.1.2[i] * n.pop.2[i] * (1 - censoring.rate)
# Calculating the information and covariance matrix
mean.vec[((i-1) * 2 + 1):((i-1) * 2 + 2)] = theta * sqrt(c((d.0.1[i] + d.1.1[i])/4, (d.0.2[i] + d.1.2[i])/4))
}
cov.mat.used = cov.mat.surv.GroupSequential.OneTreatmentArm(d.0.1, d.1.1, d.0.2, d.1.2)
# Initialize the vector
information.vector.inv = rep(NA, 2 * K)
for(i in 1:K){
information.vector.inv[((i-1)*2+1):((i-1)*2+2)] =
c(4/((d.0.1[i] + d.1.1[i])),
4/((d.0.2[i] + d.1.2[i])))
}
# Make information vector fit Tianchens code
information.vector.inv.matrix = matrix(information.vector.inv, nrow = K, byrow = TRUE)
} # End if restrict enrollment
# If enrollment at beginning of trial is restricted to sub-population 1
if(restrict.enrollment == TRUE){
# information vector in same order as the vector test statistics mentioned above.
mean.vec = rep(NA, 2 * (K-1) + 1)
# Initialize the vector
information.vector.inv = rep(NA, 2 * K - 1)
# Calculate the expected number of deaths in each treatment arm and subpopulations at each stage
d.0.1 = rep(0,K)
d.1.1 = rep(0,K)
d.0.2 = rep(0,K-1)
d.1.2 = rep(0,K-1)
# Cycle through sub-population 1
for(i in 1:K){
# Calculating the expected number of deaths for each treatment in subpopulation 1
# at interim analys i
# Note first we calcualte the probability for an individual observation and then at the end
# multiply by the sample size to get the expectation
# We cycle through the 6 different cases
if(enrollment.period >= time[i] & time[i] >= max.follow){
d.0.1[i] = (time[i] - max.follow)/time[i] * (1 - exp(-hazard.rate.pop.1[1] * max.follow)) + max.follow/time[i] * (1 - (1-exp(-hazard.rate.pop.1[1] * max.follow))/(max.follow * hazard.rate.pop.1[1]))
d.1.1[i] = (time[i] - max.follow)/time[i] * (1 - exp(-hazard.rate.pop.1[2] * max.follow)) + max.follow/time[i] * (1 - (1-exp(-hazard.rate.pop.1[2] * max.follow))/(max.follow * hazard.rate.pop.1[2]))
}
if(enrollment.period >= max.follow & max.follow >= time[i]){
d.0.1[i] = 1 - (1-exp(-hazard.rate.pop.1[1] * time[i]))/(time[i] * hazard.rate.pop.1[1])
d.1.1[i] = 1 - (1-exp(-hazard.rate.pop.1[2] * time[i]))/(time[i] * hazard.rate.pop.1[2])
}
if(time[i] >= enrollment.period & enrollment.period >= max.follow){
k = time[i] - enrollment.period
d.0.1[i] = (enrollment.period + k -max.follow)/enrollment.period * (1 - exp(-hazard.rate.pop.1[1] * max.follow)) + (max.follow - k)/enrollment.period * (1 - (exp(-hazard.rate.pop.1[1] * k) - exp(-hazard.rate.pop.1[1] * max.follow))/((max.follow - k) * hazard.rate.pop.1[1]))
d.1.1[i] = (enrollment.period + k -max.follow)/enrollment.period * (1 - exp(-hazard.rate.pop.1[2] * max.follow)) + (max.follow - k)/enrollment.period * (1 - (exp(-hazard.rate.pop.1[2] * k) - exp(-hazard.rate.pop.1[2] * max.follow))/((max.follow - k) * hazard.rate.pop.1[2]))
}
if(max.follow >= enrollment.period & enrollment.period >= time[i]){
d.0.1[i] = 1 - (1-exp(-hazard.rate.pop.1[1] * time[i]))/(time[i] * hazard.rate.pop.1[1])
d.1.1[i] = 1 - (1-exp(-hazard.rate.pop.1[2] * time[i]))/(time[i] * hazard.rate.pop.1[2])
}
if(max.follow >= time[i] & time[i] >= enrollment.period){
d.0.1[i] = 1 - (exp(-hazard.rate.pop.1[1] * (time[i]-enrollment.period)) - exp(-hazard.rate.pop.1[1] * time[i]))/(enrollment.period * hazard.rate.pop.1[1])
d.1.1[i] = 1 - (exp(-hazard.rate.pop.1[2] * (time[i]-enrollment.period)) - exp(-hazard.rate.pop.1[2] * time[i]))/(enrollment.period * hazard.rate.pop.1[2])
}
if(time[i] >= max.follow & max.follow >= enrollment.period){
d.0.1[i] = (time[i] - max.follow)/enrollment.period * (1 - exp(-hazard.rate.pop.1[1] * max.follow)) + (enrollment.period - time[i] + max.follow)/enrollment.period * (1 - (exp(-hazard.rate.pop.1[1] * (time[i]-enrollment.period)) - exp(-hazard.rate.pop.1[1] * max.follow))/((max.follow - time[i] + enrollment.period) * hazard.rate.pop.1[1]))
d.1.1[i] = (time[i] - max.follow)/enrollment.period * (1 - exp(-hazard.rate.pop.1[2] * max.follow)) + (enrollment.period - time[i] + max.follow)/enrollment.period * (1 - (exp(-hazard.rate.pop.1[2] * (time[i]-enrollment.period)) - exp(-hazard.rate.pop.1[2] * max.follow))/((max.follow - time[i] + enrollment.period) * hazard.rate.pop.1[2]))
}
# Multiplying by sample size to get expecations
d.0.1[i] = d.0.1[i] * n.pop.1[i] * (1 - censoring.rate)
d.1.1[i] = d.1.1[i] * n.pop.1[i] * (1 - censoring.rate)
# Calculating the information vector for sub-population1
if(i == 1){
mean.vec[1] = theta[1] * sqrt((d.0.1[i] + d.1.1[i])/4)
information.vector.inv[1] = 4/((d.0.1[1] + d.1.1[1]))
}
if(i != 1){
mean.vec[(i-2) *2 + 2] = theta[1] * sqrt((d.0.1[i] + d.1.1[i])/4)
information.vector.inv[(i-2) *2 + 2] = 4/((d.0.1[i] + d.1.1[i]))
}
}
# Cycle through sub-population 2
for(i in 1:(K-1)){
# Calculating the expected number of deaths for each treatment in subpopulation 1
# at interim analys i
# Note first we calcualte the probability for an individula observation and then at the end
# multiply by the sample size to get the expectation
# As enrollemnt starts at time[1] lenght of study for subpopulation 1 is
enrollment.period.2 = enrollment.period - time[1]
# and time of interim analysis since start of enrollment is
time.2 = (time - time[1])[-1]
# We cycle through the 6 different cases described in the pdf file
if(enrollment.period.2 >= time.2[i] & time.2[i] >= max.follow){
d.0.2[i] = (time.2[i] - max.follow)/time.2[i] * (1 - exp(-hazard.rate.pop.2[1] * max.follow)) + max.follow/time.2[i] * (1 - (1-exp(-hazard.rate.pop.2[1] * max.follow))/(max.follow * hazard.rate.pop.2[1]))
d.1.2[i] = (time.2[i] - max.follow)/time.2[i] * (1 - exp(-hazard.rate.pop.2[2] * max.follow)) + max.follow/time.2[i] * (1 - (1-exp(-hazard.rate.pop.2[2] * max.follow))/(max.follow * hazard.rate.pop.2[2]))
}
if(enrollment.period.2 >= max.follow & max.follow >= time.2[i]){
d.0.2[i] = 1 - (1-exp(-hazard.rate.pop.2[1] * time.2[i]))/(time.2[i] * hazard.rate.pop.2[1])
d.1.2[i] = 1 - (1-exp(-hazard.rate.pop.2[2] * time.2[i]))/(time.2[i] * hazard.rate.pop.2[2])
}
if(time.2[i] >= enrollment.period.2 & enrollment.period.2 >= max.follow){
k = time.2[i] - enrollment.period.2
d.0.2[i] = (enrollment.period.2 + k -max.follow)/enrollment.period.2 * (1 - exp(-hazard.rate.pop.2[1] * max.follow)) + (max.follow - k)/enrollment.period.2 * (1 - (exp(-hazard.rate.pop.2[1] * k) - exp(-hazard.rate.pop.2[1] * max.follow))/((max.follow - k) * hazard.rate.pop.2[1]))
d.1.2[i] = (enrollment.period.2 + k -max.follow)/enrollment.period.2 * (1 - exp(-hazard.rate.pop.2[2] * max.follow)) + (max.follow - k)/enrollment.period.2 * (1 - (exp(-hazard.rate.pop.2[2] * k) - exp(-hazard.rate.pop.2[2] * max.follow))/((max.follow - k) * hazard.rate.pop.2[2]))
}
if(max.follow >= enrollment.period.2 & enrollment.period.2 >= time.2[i]){
d.0.2[i] = 1 - (1-exp(-hazard.rate.pop.2[1] * time.2[i]))/(time.2[i] * hazard.rate.pop.2[1])
d.1.2[i] = 1 - (1-exp(-hazard.rate.pop.2[2] * time.2[i]))/(time.2[i] * hazard.rate.pop.2[2])
}
if(max.follow >= time.2[i] & time.2[i] >= enrollment.period.2){
d.0.2[i] = 1 - (exp(-hazard.rate.pop.2[1] * (time.2[i]-enrollment.period.2)) - exp(-hazard.rate.pop.2[1] * time.2[i]))/(enrollment.period.2 * hazard.rate.pop.2[1])
d.1.2[i] = 1 - (exp(-hazard.rate.pop.2[2] * (time.2[i]-enrollment.period.2)) - exp(-hazard.rate.pop.2[2] * time.2[i]))/(enrollment.period.2 * hazard.rate.pop.2[2])
}
if(time.2[i] >= max.follow & max.follow >= enrollment.period.2){
d.0.2[i] = (time.2[i] - max.follow)/enrollment.period.2 * (1 - exp(-hazard.rate.pop.2[1] * max.follow)) + (enrollment.period.2 - time.2[i] + max.follow)/enrollment.period.2 * (1 - (exp(-hazard.rate.pop.2[1] * (time.2[i]-enrollment.period.2)) - exp(-hazard.rate.pop.2[1] * max.follow))/((max.follow - time.2[i] + enrollment.period.2) * hazard.rate.pop.2[1]))
d.1.2[i] = (time.2[i] - max.follow)/enrollment.period.2 * (1 - exp(-hazard.rate.pop.2[2] * max.follow)) + (enrollment.period.2 - time.2[i] + max.follow)/enrollment.period.2 * (1 - (exp(-hazard.rate.pop.2[2] * (time.2[i]-enrollment.period.2)) - exp(-hazard.rate.pop.2[2] * max.follow))/((max.follow - time.2[i] + enrollment.period.2) * hazard.rate.pop.2[2]))
}
# Multiplying by sample size to get expecations
# Need to adjust sample size to account for late enrollment
d.0.2[i] = d.0.2[i] * n.pop.2[i+1] * (1 - censoring.rate)
d.1.2[i] = d.1.2[i] * n.pop.2[i+1] * (1 - censoring.rate)
mean.vec[((i-1) * 2 + 3)] = theta[2] * sqrt((d.0.2[i] + d.1.2[i])/4)
# Calculating the information vector for sub-population 2
information.vector.inv[((i-1) * 2 + 3)] = 4/((d.0.2[i] + d.1.2[i]))
}
# Make information vector fit Tianchens code
information.vector.inv.matrix = matrix(c(information.vector.inv[1], NA, information.vector.inv[2:length(information.vector.inv)]), nrow = K, byrow = TRUE)
cov.mat.used = cov.mat.surv.restrict.yes.GroupSequential.OneTreatmentArm(d.0.1, d.1.1, d.0.2, d.1.2)
} # End restrict enrollment
} # End if outcome = survival
if(!is.null(relative.efficiency)){
mean.vec = mean.vec * sqrt(relative.efficiency)
information.vector.inv.matrix = information.vector.inv.matrix/relative.efficiency
}
# Need to calculate the inverse to get the information vector
return(list(cov.mat.used=cov.mat.used,
non.centrality.parameter.vec = mean.vec,
information.vector = 1/information.vector.inv.matrix))
}
# This function calculates the efficacy boundaries
# Input: alpha.alloc = Vector of alpha allocations:
# The alpha allocation vector is of length 2 * number of stages
# the first two elements are the alpha allocations at stage one to each subpopulation
# the next two elements are the alpha allocations at the next stage and so on
# cov.mat.used: covariance matrix under the scenario
# err.tol = how precise is the binary search
# Output:eff.bound: The vector z_{j,k} with blocks corresponding to stages and (z_{1,k}, z_{2,k}) within stage
# eff.bound.alpha: K blocks where each block is (\tilde z_{1,k}, \tilde z_{2,k}) where block
# k corresponds to alpha reallocated if both treatments in other sub-pop are rejected at stage k
get.eff.bound.GroupSequential.OneTreatmentArm = function(alpha.allocation, cov.mat.used, err.tol = 10^-4, restrict.enrollment = FALSE){
if(restrict.enrollment == FALSE){
# Number of stages
K = length(alpha.allocation)/2
# Getting index corresponding to which sup-population is being used in each
# alpha allocation
index.sub.pop = rep(c(1, 2), K)
# eff.bound is the vector of efficacy boundaries with the elements corresponding to the
# same stage and subpopulation combinations as in the alpha.allocation vector
eff.bound = rep(NA, 2 * K)
# Cumulative alpha allocation with subpopulation 1 and 2
cum.alpha.1 = cumsum(alpha.allocation[which(index.sub.pop == 1)])
cum.alpha.2 = cumsum(alpha.allocation[which(index.sub.pop == 2)])
# Calculating the first elements of the efficacy boundary u_{j,1} corresponding to each
# subpopulation
eff.bound[1] = qnorm(1-alpha.allocation[1])
eff.bound[2] = qnorm(1-alpha.allocation[2])
# A function that calculates the cumulative type one error corresponding
# to a sub-population j
# effecacy boundaries is the efficacy boundary
# cov.mat.used is the covariance matrix
# j is the subpopulation
sign.lev = function(eff.bound.used, cov.mat.used, sub.pop.numb){
numb.stages = length(eff.bound.used)
# Index which test-statistic belongs to population and treatment, respectivly
index.sub.pop.eff = rep(c(1, 2), numb.stages)
cov.mat.eff.bound =
cov.mat.used[1:(2* numb.stages), 1:(2* numb.stages)][which(index.sub.pop.eff == sub.pop.numb), which(index.sub.pop.eff == sub.pop.numb)]
# Calculating the overall type one error under the global null
type.1.err = 1- pmvnorm(mean = rep(0, numb.stages), sigma= cov.mat.eff.bound,lower = rep(-Inf, numb.stages), upper= eff.bound.used, algorithm=GenzBretz(abseps = 0.000000001,maxpts=100000))[1]
return(type.1.err)
}
# Start calculating the efficacy boundaries associated with population 1
# Cycling through the stages after and calculating the efficacy boundary z_{1,k} for k = 2, \ldots, K
if(K > 1){
for(i in 2:K){
# Start by doing binary search for z_{1, k}
# upper and lower values of interval
upper = 20
lower = -20
length.int = upper - lower
# Initial guess
upper.bound.term = mean(c(upper, lower))
eff.bound.j = eff.bound[index.sub.pop == 1]
while(length.int > err.tol){
eff.bound.j[i] = upper.bound.term
alpha.used = sign.lev(eff.bound.j[1:i], cov.mat.used, 1)
if(alpha.used < cum.alpha.1[i]){
upper = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, lower))
}
if(alpha.used >= cum.alpha.1[i]){
lower = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, upper))
}
length.int = upper - lower
}
# "Rounding" up to preserve type 1 error
upper.bound.term = upper.bound.term + length.int
eff.bound[(i-1)*2 + 1] = upper.bound.term
}
# Calculate the efficacy boundaries associated with population 2
# Cycling through the stages and calculating the efficacy boundary
# z_{2,k} for k = 1, \ldots, K
for(i in 2:K){
# upper and lower values of interval
upper = 20
lower = -20
length.int = upper - lower
# Initial guess
upper.bound.term = mean(c(upper, lower))
eff.bound.j = eff.bound[index.sub.pop == 2]
while(length.int > err.tol){
eff.bound.j[i] = upper.bound.term
alpha.used = sign.lev(eff.bound.j[1:i], cov.mat.used, 2)
if(alpha.used < cum.alpha.2[i]){
upper = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, lower))
}
if(alpha.used >= cum.alpha.2[i]){
lower = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, upper))
}
length.int = upper - lower
}
# "Rounding" up to preserve type 1 error
upper.bound.term = upper.bound.term + length.int
eff.bound[(i-1)*2 + 2] = upper.bound.term
} # End if K >1 statement
}
# efficacy boundaries associated with alphar reallocation
# eff.bound.alpha[2*(k-1) +1] is \tilde z_{1,K} if both H_0 in subpopulation two
# are rejected at stage k
# eff.bound.alpha[2*k] is \tilde u_{2,K} if both H_0 in subpopulation one
# are rejected at stage k
eff.bound.alpha = rep(NA, 2 * K)
for(k in 1:K){
# Start with sub-population 1
# Now we calculate the efficacy boundaries for pop 1 if null hypothesis corresponding to
# pop 2 is rejected at stage k
# Start a binary search for \tilde u_{1,K}
# upper and lower values of interval
upper = 20
lower = -20
length.int = upper - lower
# Initial guess
upper.bound.term = mean(c(upper, lower))
eff.bound.j = eff.bound[index.sub.pop == 1]
# The cumulative alpha level that the last stage is allowed to test at (note not \alpha_{1,K})
# \sum_{j=1}^K \alpha_{1,j} + \sum_{j=k}^K \alpha_{2,j}
alpha.allowed = cum.alpha.1[K] + (cum.alpha.2[K] - c(0,cum.alpha.2)[k])
while(length.int > err.tol){
eff.bound.j[K] = upper.bound.term
alpha.used = sign.lev(eff.bound.j, cov.mat.used, 1)
if(alpha.used < alpha.allowed){
upper = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, lower))
}
if(alpha.used >= alpha.allowed){
lower = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, upper))
}
length.int = upper - lower
}
# "Rounding" up to preserve type 1 error
upper.bound.term = upper.bound.term + length.int
eff.bound.alpha[(k-1) * 2 + 1] = upper.bound.term
}
for(k in 1:K){
# Now sub-population 2
# Calculate the efficacy boundaries for pop 2 if both null hypothesis corresponding to
# pop 1 are rejected at stage k
# Start a binary search for \tilde u
# upper and lower values of interval
upper = 20
lower = -20
length.int = upper - lower
# Initial guess
upper.bound.term = mean(c(upper, lower))
eff.bound.j = eff.bound[index.sub.pop == 2]
# The alpha level that the last stage is allowed to test at
# \alpha_{1,K} + \sum_{j=k}^K \alpha_{2,j}
alpha.allowed = cum.alpha.2[K] + (cum.alpha.1[K] - c(0,cum.alpha.1)[k])
while(length.int > err.tol){
eff.bound.j[K] = upper.bound.term
alpha.used = sign.lev(eff.bound.j, cov.mat.used, 2)
if(alpha.used < alpha.allowed){
upper = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, lower))
}
if(alpha.used >= alpha.allowed){
lower = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, upper))
}
length.int = upper - lower
}
# "Rounding" up to preserve type 1 error
upper.bound.term = upper.bound.term + length.int
eff.bound.alpha[k*2] = upper.bound.term
} # end for k loop
}
if(restrict.enrollment == TRUE){
# Number of stages
K = (length(alpha.allocation) + 1)/2
# Getting index corresponding to which sup-population is being used in each
# alpha allocation
index.sub.pop = c(1,rep(c(1, 2), K-1))
# eff.bound is the vector of efficacy boundaries with the elements corresponding to the
# same stage and subpopulation combinations as in the alpha.allocation vector
eff.bound = rep(NA, 2 * (K-1) + 1)
# Cumulative alpha allocation with subpopulation 1 and 2
cum.alpha.1 = cumsum(alpha.allocation[which(index.sub.pop == 1)])
cum.alpha.2 = cumsum(alpha.allocation[which(index.sub.pop == 2)])
# Calculating the first elements of the efficacy boundary u_{j,1} corresponding to each
# subpopulation
eff.bound[1] = qnorm(1-alpha.allocation[1])
eff.bound[3] = qnorm(1-alpha.allocation[3])
# A function that calculates the cumulative type one error corresponding
# to a sub-population j
# effecacy boundaries is the efficacy boundary
# cov.mat.used is the covariance matrix
# sub.pop.numb is the subpopulation
sign.lev = function(eff.bound.used, cov.mat.used, sub.pop.numb){
numb.stages = length(eff.bound.used)
# Index which test-statistic belongs to population and treatment, respectivly
index.sub.pop.eff = c(1, rep(c(1, 2), numb.stages -1))
cov.mat.eff.bound =
cov.mat.used[1:(2* (numb.stages -1) +1), 1:(2* (numb.stages -1) +1)][which(index.sub.pop.eff == sub.pop.numb), which(index.sub.pop.eff == sub.pop.numb)]
numb.stages = sum(index.sub.pop.eff == sub.pop.numb)
# Calculating the overall type one error under the global null
type.1.err = 1- pmvnorm(mean = rep(0, numb.stages), sigma= cov.mat.eff.bound,lower = rep(-Inf, numb.stages), upper= eff.bound.used, algorithm=GenzBretz(abseps = 0.000000001,maxpts=100000))[1]
return(type.1.err)
}
# Start calculating the efficacy boundaries associated with population 1
# Cycling through the stages after and calculating the efficacy boundary z_{1,k} for k = 2, \ldots, K
if(K > 1){
for(i in 2:K){
# Start by doing binary search for z_{1, k}
# upper and lower values of interval
upper = 20
lower = -20
length.int = upper - lower
# Initial guess
upper.bound.term = mean(c(upper, lower))
eff.bound.j = eff.bound[index.sub.pop == 1]
while(length.int > err.tol){
eff.bound.j[i] = upper.bound.term
alpha.used = sign.lev(eff.bound.j[1:i], cov.mat.used, 1)
if(alpha.used < cum.alpha.1[i]){
upper = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, lower))
}
if(alpha.used >= cum.alpha.1[i]){
lower = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, upper))
}
length.int = upper - lower
}
# "Rounding" up to preserve type 1 error
upper.bound.term = upper.bound.term + length.int
eff.bound[(i-2)*2 + 2] = upper.bound.term
}
# Calculate the efficacy boundaries associated with population 2
# Cycling through the stages and calculating the efficacy boundary
# z_{2,k} for k = 1, \ldots, K
if(K > 2){
for(i in 2:(K-1)){
# upper and lower values of interval
upper = 20
lower = -20
length.int = upper - lower
# Initial guess
upper.bound.term = mean(c(upper, lower))
eff.bound.j = eff.bound[index.sub.pop == 2]
while(length.int > err.tol){
eff.bound.j[i] = upper.bound.term
alpha.used = sign.lev(eff.bound.j[1:i], cov.mat.used, 2)
if(alpha.used < cum.alpha.2[i]){
upper = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, lower))
}
if(alpha.used >= cum.alpha.2[i]){
lower = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, upper))
}
length.int = upper - lower
}
# "Rounding" up to preserve type 1 error
upper.bound.term = upper.bound.term + length.int
eff.bound[(i-2)*2 + 5] = upper.bound.term
} # End K > 2 statement
} # End if K >1 statement
}
# efficacy boundaries associated with alphar reallocation
# eff.bound.alpha[2*(k-1) +1] is \tilde z_{1,K} if both H_0 in subpopulation two
# are rejected
# eff.bound.alpha[2*k] is \tilde u_{2,K} if both H_0 in subpopulation one
# are rejected
eff.bound.alpha = rep(NA, 2)
# Start with sub-population 1
# Now we calculate the efficacy boundaries for pop 1 if poplation 1 is not
# strted or the null hypothesis corresponding to
# pop 2 is rejected
# Start a binary search for \tilde u_{1,K}
# upper and lower values of interval
upper = 20
lower = -20
length.int = upper - lower
# Initial guess
upper.bound.term = mean(c(upper, lower))
eff.bound.j = eff.bound[index.sub.pop == 1]
# The cumulative alpha level that the last stage is allowed to test at (note not \alpha_{1,K})
# \sum_{j=1}^K \alpha_{1,j} + \sum_{j=k}^K \alpha_{2,j}
alpha.allowed = cum.alpha.1[K] + cum.alpha.2[K-1]
while(length.int > err.tol){
eff.bound.j[K] = upper.bound.term
alpha.used = sign.lev(eff.bound.j, cov.mat.used, 1)
if(alpha.used < alpha.allowed){
upper = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, lower))
}
if(alpha.used >= alpha.allowed){
lower = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, upper))
}
length.int = upper - lower
}
# "Rounding" up to preserve type 1 error
upper.bound.term = upper.bound.term + length.int
eff.bound.alpha[1] = upper.bound.term
# Now sub-population 2
# Calculate the efficacy boundaries for pop 2 if both null hypothesis corresponding to
# pop 1 are rejected
if(K >2){
# Start a binary search for \tilde u
# upper and lower values of interval
upper = 20
lower = -20
length.int = upper - lower
# Initial guess
upper.bound.term = mean(c(upper, lower))
eff.bound.j = eff.bound[index.sub.pop == 2]
# The alpha level that the last stage is allowed to test at
# \alpha_{1,K} + \sum_{j=k}^K \alpha_{2,j}
alpha.allowed = cum.alpha.2[K-1] + cum.alpha.1[K]
while(length.int > err.tol){
eff.bound.j[K-1] = upper.bound.term
alpha.used = sign.lev(eff.bound.j, cov.mat.used, 2)
if(alpha.used < alpha.allowed){
upper = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, lower))
}
if(alpha.used >= alpha.allowed){
lower = upper.bound.term
upper.bound.term = mean(c(upper.bound.term, upper))
}
length.int = upper - lower
}
# "Rounding" up to preserve type 1 error
upper.bound.term = upper.bound.term + length.int
eff.bound.alpha[2] = upper.bound.term
}
# If only two stages
if(K == 2){
alpha.allowed = cum.alpha.2[K-1] + cum.alpha.1[K]
eff.bound.alpha[2] = qnorm(1-alpha.allowed)
}
}
return(list(eff.bound = eff.bound, eff.bound.alpha = eff.bound.alpha))
}
# This function evaluates the performance of a given design
# Inputs: test.statistics: A matrix of test statistics where each row is a vector of test statistics
# efficacy.boundary: all the different effacacy boundaries outputted from get.eff.bound