Determines the optimal dose to assign the next patient cohort.

Description

This function returns the optimal acceptable dose number to assign the next patient cohort or stops the trial if no dose is deemed acceptable.

Usage

AssignEffTox(YE, YT, Doses, Dose, DosesTried, Hypermeans, Hypervars,
  Contour, PiLim, ProbLim, B)

Arguments

Value

The optimal dose level to administer the next patient cohort.

Examples

##Doses, YE,YT
Doses= c(1,1,1,2,2,2,1,1,1,3,3,3,1,1,1,2,2,2)
YE = c(0,0,1,1,1,0,0,0,0,1,1,1,0,0,1,1,1,0)
YT=c(0,0,0,1,1,0,1,0,0,1,1,1,0,0,0,1,0,0)
##Vector of Numerical Doses
Dose = c(1,2,3,3.5,5)
Dose=(Dose-mean(Dose))/sd(Dose)
##Five doses, but only 3 tried so we have
DosesTried=c(1,1,1,0,0)
## Contour Vector
Contour = c(.35, .75,.7,.4)
##Hypermeans
Hypermeans = c(.022,3.45,0,-4.23,3.1,0)
Hypervars = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1)
Hypervars=Hypervars^2
##Acceptability Criteria
PiLim = c(.3,.4)
ProbLim=c(.1,.1)
##Number of iterations
B=2000
AssignEffTox(YE,YT, Doses, Dose, DosesTried, Hypermeans,  Hypervars, Contour, PiLim,  ProbLim, B )

Obtains estimated posterior probabilities of the four outcomes of (YE,YT) for each dose.

Description

This function is used in Reoptimize, SimPhase123 and SimPhase3, here we estimate the mixture probabilities over the four outcomes for efficacy and toxicity.

Usage

EFFTOX(YE, YT, Doses, Dose, Hypermeans, Hypervars, B)

Arguments

Value

The posterior probability matrix for the events (YE,YT) in each row corresponding to a dose level.

Examples

##Doses, YE,YT
Doses= c(1,1,1,2,2,2,1,1,1,3,3,3,1,1,1,2,2,2)
YE = c(0,0,1,1,1,0,0,0,0,1,1,1,0,0,1,1,1,0)
YT=c(0,0,0,1,1,0,1,0,0,1,1,1,0,0,0,1,0,0)
##Vector of Numerical Doses
Dose = c(1,2,3,3.5,5)
Dose=(Dose-mean(Dose))/sd(Dose)
Doses=Dose[Doses]
##Hypermeans
Hypermeans = c(.022,3.45,0,-4.23,3.1,0)
Hypervars = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1)
Hypervars=Hypervars^2
##Number of iterations
B=2000
EFFTOX(YE,YT, Doses, Dose, Hypermeans,  Hypervars, B )

Returns posterior distribution for key mixture model parameters

Description

This function performs MCMC with Metropolis-Hastings-Green steps for the baseline hazard function and is used in the functions Reoptimize, SimPhase123 and SimPhase3.

Usage

PieceMCMC(Y, I, YE, YT, Doses, Dose, B, prob, MaxObs)

Arguments

Value

Returns a list containing a matrix of posterior means for each dose, regression coefficients in the cox models, locations of the split points, log hazard heights on each interval, and the number of intervals in the baseline hazard.

Examples

n=100
Y=rexp(n,1)
I = rbinom(n,1,.9)
YE = rbinom(n,1,.5)
YT = rbinom(n,1,.5)
Dose = c(1,2,3,3.5,5)
Dose=(Dose-mean(Dose))/sd(Dose)
Doses = sample(1:5,n,replace=TRUE)
Doses=Dose[Doses]
B=2000
MaxObs = matrix(rep(0,length(Dose)*4),nrow=4)
prob=matrix(rep(0,length(Dose)*4),ncol=4)
prob=prob+1/4
MaxObs=MaxObs+max(Y)
G=PieceMCMC(Y,I,YE,YT,Doses,Dose,B,prob,MaxObs)

Randomizes Eff-Tox dose proportional to posterior desireability scores.

Description

This function returns a random acceptable dose number to assign the next patient cohort or stops the trial if no dose is deemed acceptable.

Usage

RandomEffTox(YE, YT, Doses, Dose, DosesTried, Hypermeans, Hypervars,
  Contour, PiLim, ProbLim, B)

Arguments

Value

A random dose level to administer the next patient cohort.

Examples

##Doses, YE,YT
Doses= c(1,1,1,2,2,2,1,1,1,3,3,3,1,1,1,2,2,2)
YE = c(0,0,1,1,1,0,0,0,0,1,1,1,0,0,1,1,1,0)
YT=c(0,0,0,1,1,0,1,0,0,1,1,1,0,0,0,1,0,0)
##Vector of Numerical Doses
Dose = c(1,2,3,3.5,5)
Dose=(Dose-mean(Dose))/sd(Dose)
##Five doses, but only 3 tried so we have
DosesTried=c(1,1,1,0,0)
## Contour Vector
Contour = c(.35, .75,.7,.4)
##Hypermeans
Hypermeans = c(.022,3.45,0,-4.23,3.1,0)
Hypervars = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1)
Hypervars=Hypervars^2
##Acceptability Criteria
PiLim = c(.3,.4)
ProbLim=c(.1,.1)
##Number of iterations
B=2000
RandomEffTox(YE,YT, Doses, Dose, DosesTried, Hypermeans,  Hypervars, Contour, PiLim,  ProbLim, B )

Gives the Optimal Dose for enrolling next patient cohort.

Description

This function returns the optimal dose number to assign the next patient cohort or stops the trial if no dose is deemed acceptable.

Usage

Reoptimize(Y, I, YE, YT, Doses, Dose, Hypermeans, Hypervars, B)

Arguments

References

[1] Chapple and Thall (2018).A Hybrid Phase I-II/III Clinical Trial Design Allowing Dose Re-Optimization in Phase III. Biometrics. In Press,

Examples

##Doses, YE,YT
Doses= c(1,1,1,2,2,2,1,1,1,3,3,3,1,1,1,2,2,2)
YE = c(0,0,1,1,1,0,0,0,0,1,1,1,0,0,1,1,1,0)
YT=c(0,0,0,1,1,0,1,0,0,1,1,1,0,0,0,1,0,0)
Y=rexp(length(YE))
I=rbinom(length(YE),1,.9)
##Vector of Numerical Doses
Dose = c(1,2,3,3.5,5)
Dose=(Dose-mean(Dose))/sd(Dose)
##Hypermeans for Eff-Tox
Hypermeans = c(.022,3.45,0,-4.23,3.1,0)
Hypervars = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1)
Hypervars=Hypervars^2
###Number of iterations
B=20000
Reoptimize(Y,I,YE,YT, Doses, Dose, Hypermeans,  Hypervars,B)

Gives the Optimal Dose for enrolling next patient cohort. Used in the SimPhase123 function.

Description

This function returns the optimal dose number to assign the next patient cohort or stops the trial if no dose is deemed acceptable.

Usage

Reoptimize1(Y, I, YE, YT, Doses, Dose, Hypermeans, Hypervars, B)

Arguments

References

[1] Chapple and Thall (2018).A Hybrid Phase I-II/III Clinical Trial Design Allowing Dose Re-Optimization in Phase III. Biometrics. In Press,

Examples

##Doses, YE,YT
Doses= c(1,1,1,2,2,2,1,1,1,3,3,3,1,1,1,2,2,2)
YE = c(0,0,1,1,1,0,0,0,0,1,1,1,0,0,1,1,1,0)
YT=c(0,0,0,1,1,0,1,0,0,1,1,1,0,0,0,1,0,0)
Y=rexp(length(YE))
I=rbinom(length(YE),1,.9)
##Vector of Numerical Doses
Dose = c(1,2,3,3.5,5)
Dose=(Dose-mean(Dose))/sd(Dose)
##Hypermeans for Eff-Tox
Hypermeans = c(.022,3.45,0,-4.23,3.1,0)
Hypervars = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1)
Hypervars=Hypervars^2
###Number of iterations
B=20000
Reoptimize1(Y,I,YE,YT, Doses, Dose, Hypermeans,  Hypervars,B)

Gives true mean survival times for the control therapy.

Description

Returns the mean survival times for the control given efficacy and toxicity dose probability vector, distribution family and linear relationship, effiacy, toxicity and survival.

Usage

ReturnMeanControl(ProbC, betaC, Family, alpha)

Arguments

References

[1] Chapple and Thall (2018).A Hybrid Phase I-II/III Clinical Trial Design Allowing Dose Re-Optimization in Phase III. Biometrics. In Press,

Examples

###Family of Distributions
Family="Gamma"
###Shape parameter
alpha=2
##True beta vector for efficacy, toxicity and intercept of the control treatment
betaC=c(.3,-.25,2.389)
##True efficacy and toxicity probability for control group
ProbC = c(.4,.15)
ReturnMeanControl(ProbC,betaC,Family,alpha)

Gives true mean survival times for doses considered of the experimental agent.

Description

Returns the dose specific mean survival times for given efficacy and toxicity dose probability vector, distribution family and linear relationship between dose, effiacy, toxicity and survival.

Usage

ReturnMeansAgent(PE, PT, beta, Dose, Family, alpha)

Arguments

References

[1] Chapple and Thall (2018). A Hybrid Phase 12/3 Clinical Trial Design Allowing Dose Re-Optimization in Phase 3 Biometrics. Under Review.

Examples

##True Efficacy and Toxicity Probabilities
PT = c(.1,.15,.25,.35,.5)
PE=c(.2,.4,.6,.65,.7)
##Dose Levels considered
Dose = c(1,2,3,3.5,5)
Dose=(Dose-mean(Dose))/sd(Dose)
###Family of Distributions
Family="Gamma"
###Shape parameter ## Doesn't matter for exponential distribution
alpha=2
###True Beta vector
beta = c(.75,-.5, .3, -.25,2.143)
ReturnMeansAgent(PE,PT,beta,Dose,Family,alpha)

Gives operating characteristics of phase 123 and conventional design.

Description

Returns the probability of selecting the optimal dose, type I error, generalized power, probability of making the best decision, average number of patients treated and average trial duration.

Usage

ReturnOCS(Results, Means, CMu, Delta, Hyp)

Arguments

References

[1] Chapple and Thall (2018). A Hybrid Phase 12/3 Clinical Trial Design Allowing Dose-Re-Optimization in Phase 3 Biometrics. Under Review.

Examples

##True Mean Control
CMu=24
##True Means Agent
Means = c(27,32,38,42,28)
##Desired improvement in mean survival
Delta=12
##Random Trial results
 Results=as.list(c(0,0))
 nSims=5
 X=matrix(rep(NA,nSims*4),nrow=nSims)
 ##DoseSelected
 X[,1]=c(2,3,4,4,3)
 X[,2]=c(0,1,1,1,1)
 X[,3]=c(270,500,500,420,400)
 X[,4]=c(70,85,88,70,88)
 Results[[1]]=X
 X[,1]=c(2,3,5,4,2)
 X[,2]=c(0,1,0,1,0)
 X[,3]=c(270,500,450,420,415)
 X[,4]=c(70,82,80,70,79)
Results[[2]]=X
Hyp=1
ReturnOCS(Results,Means,CMu,Delta,Hyp)

Simulates repitions of an Adaptive Eff-Tox Trial.

Description

This function simulates repititions of an adaptive Eff-Tox Trial and returns a list containing the optimal dose chosen

Usage

RunAdaptiveEffToxTrial(DoseStart, Dose, Hypermeans, Hypervars, Contour,
  PiLim, ProbLim, cohort, NET, NF, B, nSims, PETrue, PTTrue)

Arguments

Value

List containing the vector of optimal doses chosen, a matrix of posterior desireability scores for each trial, and a matrix consisting of patient dose assignments and Toxicity and Efficacy indicators, with each Nmax rows corresponding to a separate trial. Trials that are stopped due to excessive toxicity probabilty or small efficacy probabilities are not included in the final results.

Examples

##Doses, YE,YT
##Starting Dose
DoseStart=1
##Vector of Numerical Doses
Dose = c(1,2,3,3.5,5)
Dose=(Dose-mean(Dose))/sd(Dose)
## Contour Vector
Contour = c(.35, .75,.7,.4)
##Hypermeans
Hypermeans = c(.022,3.45,0,-4.23,3.1,0)
Hypervars = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1)
Hypervars=Hypervars^2
##Acceptability Criteria
PiLim = c(.3,.4)
ProbLim=c(.1,.1)
##Cohort Size, N^F and N_ET
cohort=3
NF=15
NET=30
PTTrue = c(.1,.15,.25,.35,.5)
PETrue=c(.2,.4,.6,.65,.7)
##Number of iterations for MCMC
B=2000
### Number of Simulations
nSims=1
RunAdaptiveEffToxTrial(DoseStart,Dose, Hypermeans,  Hypervars,
Contour, PiLim, ProbLim,  cohort, NET,  NF, B, nSims, PETrue, PTTrue )

Simulates replications of the phase123 and phase 12-3 trials.

Description

This function simulates replications of the phase123 and phase 12-3 trials and returns a list containing the doses chosen, decisions made (1=A(x) better, 0= futility, -1=C better)

Usage

SimPhase123(DoseStart, Dose, PE, PT, Hypermeans, Hypervars, Contour, PiLim,
  ProbLim, NET, NF, Accrue12, Time12, cohort, betaA, ProbC, betaC, Family,
  alpha, Nmax, Accrue, Twait, NLookSwitch, NLook, Sup, Fut, nSims)

Arguments

References

[1] Chapple and Thall (2018).A Hybrid Phase I-II/III Clinical Trial Design Allowing Dose Re-Optimization in Phase III. Biometrics. In Press,

Examples

 ##We need to specify Phase 12,
###Phase 3 trial paramters,
##the additional phase 123 parameters and simulation parameters
#This is scenario 3 for the exponetial case
##the additional phase 123 parameters and simulation parameters
###########PHASE12 Parameters ##################
DoseStart=1
##True Efficacy and Toxicity Probabilities
PT = c(.05,.08,.1,.15,.2)
PE=c(.2,.25,.35,.4,.55)
##Raw Dose Levels considered
Dose = c(1,2,3,3.5,5)
#Max Sample Size
NET=30
##Number of patients before randomization
NF=15
##Cohort size
cohort=3
##Hypermeans for Eff-Tox
Hypermeans = c(.022,3.45,0,-4.23,3.1,0)
Hypervars = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1)
Hypervars=Hypervars^2
##Contour Vector
Contour = c(.35, .75,.7,.4)
##Acceptability Criteria
PiLim = c(.3,.4)
ProbLim=c(.1,.1)
##Phase 12 accrual rate
Accrue12=5
###How long is the time window in phase 12?
Time12=1
##########PHASE3 Parameters####################
Nmax=500
##Number of patient events for interim looks
NLook = c(200,300,400)
##Superiority Boundaries
Sup = c(2.96, 2.53,1.99)
##Futility Boundaries (0 means no futility decision)
Fut = c(0,1.001,0)
##Average accrual rate for phase III
Accrue = 10
###########Phase123 Parameters###########
###Number of patient events to re-optimize doses
NLookSwitch=50
##Time in between phase 12 and phase 3
Twait=1
#########Simulation Parameters######
###Family of Distributions
Family="Gamma"
###Shape parameter, Not needed for Exponential
alpha=1
###True Beta vector (beta_1,exp(beta_E),-exp(beta_T),beta_2,beta_0)
betaA = c(.1, .3, -1,-1,3.6)
##True beta vector for (exp(beta_E),-exp(beta_T),beta_C)  of the control treatment
betaC=c(.3,-1,log(24/1.035111))
##True efficacy and toxicity probability for control group
ProbC = c(.3,.1)
##Number of simulations to run
nSims=1
##Run Simulations
Results=SimPhase123(DoseStart,Dose,PE,PT,Hypermeans,Hypervars,Contour,
                 PiLim,ProbLim,NET,NF,Accrue12,Time12,cohort,betaA,ProbC,betaC,
                Family,alpha,Nmax,Accrue,Twait,NLookSwitch,NLook,Sup,Fut,nSims)

Performs one replication of phase 3 for the phase 123 design, given phase 12 data.

Description

This function simulates the phase 3 potion of the phase 123 trial, given phase 12 outcomes.

Usage

SimPhase3(Dose, Phase12, PE, PT, Hypermeans, Hypervars, betaA, ProbC,
  betaC, Family, alpha, Nmax, Opt, Accrue, Time12, Twait, NLookSwitch,
  NLook, Sup, Fut)

Arguments

References

[1] Chapple and Thall (2018).A Hybrid Phase I-II/III Clinical Trial Design Allowing Dose Re-Optimization in Phase III. Biometrics. In Press,

Examples

library(survival)
##True Efficacy and Toxicity Probabilities
PT = c(.1,.15,.25,.35,.5)
PE=c(.2,.4,.6,.65,.7)
##Dose Levels considered
Dose = c(1,2,3,3.5,5)
Dose=(Dose-mean(Dose))/sd(Dose)
##Average accrual rate for phase III
Accrue = 10
#'##Hypermeans for Eff-Tox
Hypermeans = c(.022,3.45,0,-4.23,3.1,0)
Hypervars = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1)
Hypervars=Hypervars^2
Contour = c(.35, .75,.7,.4)
PiLim = c(.3,.4)
ProbLim=c(.1,.1)
###Family of Distributions
Family="Exponential"
###Shape parameter ## Doesn't matter for exponential distribution
alpha=1
###True Beta vector
betaA = c(.75,-.5, .3, -.25,2.143)
##True beta vector for efficacy, toxicity and intercept of the control treatment
betaC=c(.3,-.25,2.389)
##True efficacy and toxicity probability for control group
ProbC = c(.4,.15)
##Waiting time in between
Twait=1
###How long is the time window in phase 12?
Time12=1
##Dose to start phase 3 with
Opt=3
##Make matrix with old phase 12 data
Doses= c(1,1,1,2,2,2,1,1,1,3,3,3,1,1,1,2,2,2)
YE = c(0,0,1,1,1,0,0,0,0,1,1,1,0,0,1,1,1,0)
YT=c(0,0,0,1,1,0,1,0,0,1,1,1,0,0,0,1,0,0)
##Accrual Times for old data
Accrue12=2
##Size of phase 12 cohort
cohort=3
ACC1=cumsum(rexp(length(YT),Accrue12))
##Accrual times are the same for each cohort in phase 12
Grab = rep(NA,length(YT)/cohort)
for(m in 1:length(Grab)){Grab[m]=ACC1[m*3]}
for(m in 1:length(Grab)){ACC1[((m-1)*cohort+1):((m-1)*cohort+cohort)]=rep(Grab[m],cohort)}
Phase12 = cbind(Doses,YE,YT,ACC1)
betaC=c(.3,-.25,2.389)
##True efficacy and toxicity probability for control group
ProbC = c(.4,.15)
##Max Sample Size
Nmax=500
###Number of patient events to Re-optimize doses
NLookSwitch = 50
##Number of patient events for interim looks
NLook = c(200,300,400)
##Superiority Boundaries
Sup = c(2.96, 2.53,1.99)
##Futility Boundaries (0 means no futility decision)
Fut = c(0,1.001,0)
##Starting Dose, hat(x)_ET
Opt=3
##Number of simulations to run
nSims=10
SimPhase3(Dose,Phase12,PE,PT,Hypermeans,Hypervars,betaA,
ProbC,betaC,Family,alpha,Nmax,Opt,Accrue,
Time12,Twait,NLookSwitch,NLook,Sup,Fut)