| Title: | Simulates SPSO and Efftox Phase 12 Trials with Correlated Outcomes |
|---|---|
| Description: | Simulating and conducting four phase 12 clinical trials with correlated binary bivariate outcomes described. Uses the 'Efftox' (efficacy and toxicity tradeoff, <https://biostatistics.mdanderson.org/SoftwareDownload/SingleSoftware/Index/2>) and SPSO (Semi-Parametric Stochastic Ordering) models with Utility and Desirability based objective functions for dose finding. |
| Authors: | Andrew G Chapple |
| Maintainer: | Andrew G Chapple <[email protected]> |
| License: | GPL-2 |
| Version: | 1.5 |
| Built: | 2024-10-31 21:17:31 UTC |
| Source: | CRAN |
This function returns the optimal acceptable dose number to assign the next patient cohort or stops the trial if no dose is deemed acceptable.
AssignEffTox( YE, YT, Doses, Dose, DosesTried, Hypermeans, Hypervars, Contour, PiLim, ProbLim, B )AssignEffTox( YE, YT, Doses, Dose, DosesTried, Hypermeans, Hypervars, Contour, PiLim, ProbLim, B )
YE |
Vector containing observed efficacy indicators. |
YT |
Vector containing observed toxicity indicators. |
Doses |
Vector containing numbered Doses of patients in trial. |
Dose |
Vector containing the standardized doses considered. |
DosesTried |
Binary vector corresponding to which doses have been tried. |
Hypermeans |
Vector containing prior hypermeans of length 6 for Eff-Tox parameters. |
Hypervars |
Vector containing prior hypervariances of length 6 for Eff-Tox parameters. |
Contour |
Vector containing 4 entries used to make the desireability function. Contour[1] contains a desired toxicity probability given efficacy, Countour[2] contains a desired efficacy probability given toxicity, and (Contour[3],Contour[4]) is an equally desireable pair of efficacy and toxicity probabilities that are non-zero or one. |
PiLim |
Vector of length two with PiLim[1] containing the acceptable lower limit on efficacy probability and PiLim[2] containing the acceptable upper limit on toxicity probability. |
ProbLim |
Vector of length two with ProbLim[1] containing the probability cutoff for acceptable efficacy probability and ProbLim[2] containing the probability cutoff for acceptable toxicity probability. |
B |
Number of iterations to perform in the MCMC. |
The optimal dose level to administer the next patient cohort.
##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 )##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 )
This function returns the optimal acceptable dose number to assign the next patient cohort or stops the trial if no dose is deemed acceptable.
AssignEffToxUT( YE, YT, Doses, Dose, DosesTried, Hypermeans, Hypervars, UT, PiLim, ProbLim, B )AssignEffToxUT( YE, YT, Doses, Dose, DosesTried, Hypermeans, Hypervars, UT, PiLim, ProbLim, B )
YE |
Vector containing observed efficacy indicators. |
YT |
Vector containing observed toxicity indicators. |
Doses |
Vector containing numbered Doses of patients in trial. |
Dose |
Vector containing the standardized doses considered. |
DosesTried |
Binary vector corresponding to which doses have been tried. |
Hypermeans |
Vector containing prior hypermeans of length 6 for Eff-Tox parameters. |
Hypervars |
Vector containing prior hypervariances of length 6 for Eff-Tox parameters. |
UT |
2x2 utility matrix. |
PiLim |
Vector of length two with PiLim[1] containing the acceptable lower limit on efficacy probability and PiLim[2] containing the acceptable upper limit on toxicity probability. |
ProbLim |
Vector of length two with ProbLim[1] containing the probability cutoff for acceptable efficacy probability and ProbLim[2] containing the probability cutoff for acceptable toxicity probability. |
B |
Number of iterations to perform in the MCMC. |
The optimal dose level to administer the next patient cohort.
Generates binary bivariate data from a 2x2 matrix of probabilities.
GETBIN(PVEC)GETBIN(PVEC)
PVEC |
Contains TRUE pi00,pi10, pi01, pi11 for each dose |
Binary bivariate random variate (YE,YT).
Takes estimated posterior mean efficacy and toxicity values and returns the posterior mean desireability score for a given tradeoff contour.
GetDesire(PE, PT, Contour)GetDesire(PE, PT, Contour)
PE |
True or estimated probability of efficacy. |
PT |
True of estimated probability of toxicity. |
Contour |
Vector containing 4 entries used to make the desireability function. Contour(1) contains a desired toxicity probability given efficacy, Countour(2) contains a desired efficacy probability given toxicity, and (Contour(3),Contour(4)) is an equally desireable pair of efficacy and toxicity probabilities that are non zero or one. |
Computes the real-valued desireability for an estimated (PE, PT) pair and elicited co
PE=.6 PT=.2 ##Contour values Contour=c(.35,.7,.8,.6) GetDesire(PE,PT,Contour)PE=.6 PT=.2 ##Contour values Contour=c(.35,.7,.8,.6) GetDesire(PE,PT,Contour)
Provides the optimal dose level as determined by the Efftox model, posterior mean toxicity probability, efficacy probability, and desirability of each dose level. Doses that are unacceptably toxic or inefficous have a desirability of 0.
GetEfftoxContour( YE, YT, Doses, Dose, Dosetried, cohort, Contour, CutE, CutT, AcceptE, AcceptT, HypermeansEFF, HypervarsEFF, B )GetEfftoxContour( YE, YT, Doses, Dose, Dosetried, cohort, Contour, CutE, CutT, AcceptE, AcceptT, HypermeansEFF, HypervarsEFF, B )
YE |
Length n binary indicator vector of efficacy status. |
YT |
Length n binary indicator vector of toxicity status. |
Doses |
Length n vector of integer Doses given to patients. |
Dose |
Vector of standardized log-dose levels considered in the trial. |
Dosetried |
Binary vector corresponding to which doses have been tried. |
cohort |
Cohort size for the trial. |
Contour |
Contour vector for desirability function. Contains in order: (pi_1,E,pi_2,T,pi_3,E,pi_4,T). |
CutE |
Cutoff for efficacy probability acceptability. Dose-efficacy probabilities must be larger than this for patient assignment. |
CutT |
Cutoff for toxicity probability acceptability. Dose-toxicity probabilities must be smaller than this for patient assignment. |
AcceptE |
Posterior probability threshold for efficacy acceptability. |
AcceptT |
Posterior probability threshold for toxicity acceptability. |
HypermeansEFF |
Hypermeans for the Efftox model. In order, entries are hypermeans for (beta_0,E,beta_1,E,beta_2,E,beta_0,T,beta_1,T,psi). |
HypervarsEFF |
Hypervariances for the Efftox model. In order, entries are hypervariances for (beta_0,E,beta_1,E,beta_2,E,beta_0,T,beta_1,T,psi). |
B |
Number of iterations to run for the MCMC. |
A list containing the optimal dose level to assign the next patient cohort in the first entry and a matrix in the second entry, with rows corresponding to (1) the dose #, (2) mean posterior toxicity probability at each dose, mean posterior efficacy probability at each dose, and acceptable posterior desirability score of each dose level (0s indicate the dose is not acceptably efficous or toxic).
##Get the Data ##True Tox Prob PT = c(.1,.2,.25,.5,.7) ##True EFF Prob PE = c(.3,.4,.7,.5,.5) Doses=c(1,1,1,2,2,2,3,3,3,2,2,2,1,1,1,2,2,2,3,3,3) YE=Doses YT=Doses Dosetried=rep(0,length(PE)) ##Generate data for(k in 1:length(PE)){ if(sum(Doses==k)){ ##Dose level has been tried Dosetried[k]=1 YE[Doses==k]=rbinom(sum(Doses==k),1,PE[k]) YT[Doses==k]=rbinom(sum(Doses==k),1,PT[k]) } } ##Hypermeans and hypervariances HypermeansEFF = c(.022,3.45,0,-4.23,3.1,0) HypervarsEFF = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1) HypervarsEFF=HypervarsEFF^2 ##Vector of Numerical Doses Dose = c(1,2,3,3.5,5) ##Dose for Eff-Tox Dose=log(Dose)-mean(log(Dose)) ##Trial parameters cohort=3 ##Contour vector Contour = c(.35, .75,.7,.4) CutE=.3 ##Efficacy threshold CutT=.4 ##Toxicity threshold AcceptE=.3 ##Eff acceptability threshold AcceptT= .3 ##Tox acceptability threshold B=100##Number of iterations GetEfftoxContour(YE, YT,Doses,Dose, Dosetried, cohort, Contour,CutE, CutT, AcceptE, AcceptT, HypermeansEFF, HypervarsEFF,B )##Get the Data ##True Tox Prob PT = c(.1,.2,.25,.5,.7) ##True EFF Prob PE = c(.3,.4,.7,.5,.5) Doses=c(1,1,1,2,2,2,3,3,3,2,2,2,1,1,1,2,2,2,3,3,3) YE=Doses YT=Doses Dosetried=rep(0,length(PE)) ##Generate data for(k in 1:length(PE)){ if(sum(Doses==k)){ ##Dose level has been tried Dosetried[k]=1 YE[Doses==k]=rbinom(sum(Doses==k),1,PE[k]) YT[Doses==k]=rbinom(sum(Doses==k),1,PT[k]) } } ##Hypermeans and hypervariances HypermeansEFF = c(.022,3.45,0,-4.23,3.1,0) HypervarsEFF = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1) HypervarsEFF=HypervarsEFF^2 ##Vector of Numerical Doses Dose = c(1,2,3,3.5,5) ##Dose for Eff-Tox Dose=log(Dose)-mean(log(Dose)) ##Trial parameters cohort=3 ##Contour vector Contour = c(.35, .75,.7,.4) CutE=.3 ##Efficacy threshold CutT=.4 ##Toxicity threshold AcceptE=.3 ##Eff acceptability threshold AcceptT= .3 ##Tox acceptability threshold B=100##Number of iterations GetEfftoxContour(YE, YT,Doses,Dose, Dosetried, cohort, Contour,CutE, CutT, AcceptE, AcceptT, HypermeansEFF, HypervarsEFF,B )
Provides the optimal dose level as determined by the Efftox model, posterior mean toxicity probability, efficacy probability, and mean utility of each dose level. Doses that are unacceptably toxic or inefficous have a utility of 0.
GetEfftoxUt( YE, YT, Doses, Dose, Dosetried, cohort, UT, CutE, CutT, AcceptE, AcceptT, HypermeansEFF, HypervarsEFF, B )GetEfftoxUt( YE, YT, Doses, Dose, Dosetried, cohort, UT, CutE, CutT, AcceptE, AcceptT, HypermeansEFF, HypervarsEFF, B )
YE |
Length n binary indicator vector of efficacy status. |
YT |
Length n binary indicator vector of toxicity status. |
Doses |
Length n vector of integer Doses given to patients. |
Dose |
Vector of standardized log-dose levels considered in the trial. |
Dosetried |
Binary vector corresponding to which doses have been tried. |
cohort |
Cohort size for the trial. |
UT |
Utility matrix for the four bivariate (efficacy, toxicity) events. |
CutE |
Cutoff for efficacy probability acceptability. Dose-efficacy probabilities must be larger than this for patient assignment. |
CutT |
Cutoff for toxicity probability acceptability. Dose-toxicity probabilities must be smaller than this for patient assignment. |
AcceptE |
Posterior probability threshold for efficacy acceptability. |
AcceptT |
Posterior probability threshold for toxicity acceptability. |
HypermeansEFF |
Hypermeans for the Efftox model. In order, entries are hypermeans for (beta_0,E,beta_1,E,beta_2,E,beta_0,T,beta_1,T,psi). |
HypervarsEFF |
Hypervariances for the Efftox model. In order, entries are hypervariances for (beta_0,E,beta_1,E,beta_2,E,beta_0,T,beta_1,T,psi). |
B |
Number of iterations to run for the MCMC. |
A list containing the optimal dose level to assign the next patient cohort in the first entry and a matrix in the second entry, with rows corresponding to (1) the dose #, (2) mean posterior toxicity probability at each dose, mean posterior efficacy probability at each dose, and acceptable posterior utility score of each dose level (0s indicate the dose is not acceptably efficous or toxic).
##Get the Data ##True Tox Prob PT = c(.1,.2,.25,.5,.7) ##True EFF Prob PE = c(.3,.4,.7,.5,.5) Doses=c(1,1,1,2,2,2,3,3,3,2,2,2,1,1,1,2,2,2,3,3,3) YE=Doses YT=Doses Dosetried=rep(0,length(PE)) ##Generate data for(k in 1:length(PE)){ if(sum(Doses==k)){ ##Dose level has been tried Dosetried[k]=1 YE[Doses==k]=rbinom(sum(Doses==k),1,PE[k]) YT[Doses==k]=rbinom(sum(Doses==k),1,PT[k]) } } ##Hypermeans and hypervariances HypermeansEFF = c(.022,3.45,0,-4.23,3.1,0) HypervarsEFF = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1) HypervarsEFF=HypervarsEFF^2 ##Vector of Numerical Doses Dose = c(1,2,3,3.5,5) ##Dose for Eff-Tox Dose=log(Dose)-mean(log(Dose)) ##Trial parameters cohort=3 ##Utility Matrix UT = matrix(c(38.23529,100,0,61.76471),nrow=2,byrow=TRUE) CutE=.3 ##Efficacy threshold CutT=.4 ##Toxicity threshold AcceptE=.3 ##Eff acceptability threshold AcceptT= .3 ##Tox acceptability threshold B=100##Number of iterations GetEfftoxUt(YE, YT,Doses,Dose, Dosetried, cohort, UT,CutE, CutT, AcceptE, AcceptT, HypermeansEFF, HypervarsEFF,B )##Get the Data ##True Tox Prob PT = c(.1,.2,.25,.5,.7) ##True EFF Prob PE = c(.3,.4,.7,.5,.5) Doses=c(1,1,1,2,2,2,3,3,3,2,2,2,1,1,1,2,2,2,3,3,3) YE=Doses YT=Doses Dosetried=rep(0,length(PE)) ##Generate data for(k in 1:length(PE)){ if(sum(Doses==k)){ ##Dose level has been tried Dosetried[k]=1 YE[Doses==k]=rbinom(sum(Doses==k),1,PE[k]) YT[Doses==k]=rbinom(sum(Doses==k),1,PT[k]) } } ##Hypermeans and hypervariances HypermeansEFF = c(.022,3.45,0,-4.23,3.1,0) HypervarsEFF = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1) HypervarsEFF=HypervarsEFF^2 ##Vector of Numerical Doses Dose = c(1,2,3,3.5,5) ##Dose for Eff-Tox Dose=log(Dose)-mean(log(Dose)) ##Trial parameters cohort=3 ##Utility Matrix UT = matrix(c(38.23529,100,0,61.76471),nrow=2,byrow=TRUE) CutE=.3 ##Efficacy threshold CutT=.4 ##Toxicity threshold AcceptE=.3 ##Eff acceptability threshold AcceptT= .3 ##Tox acceptability threshold B=100##Number of iterations GetEfftoxUt(YE, YT,Doses,Dose, Dosetried, cohort, UT,CutE, CutT, AcceptE, AcceptT, HypermeansEFF, HypervarsEFF,B )
Uses elicited efficacy and toxicity dose-specific parameters along withlatent prior variance, dose-specific mean hypervariance, frailty variance, and global probability of monotonicity to determine dose-specific prior means for efficacy and toxicityand prints the prior effective sample size associated with the specified prior parameters.
GetPriorsSPSO(PROBST, PROBSE, tau, Var, HypVar, PGLOBAL)GetPriorsSPSO(PROBST, PROBSE, tau, Var, HypVar, PGLOBAL)
PROBST |
Elicited prior toxicity probability at each dose. |
PROBSE |
Elicted prior efficacy probability at each dose. |
tau |
Frailty variance parameter. |
Var |
Latent parameter variance for normal probability of efficacy and toxicity. |
HypVar |
Hypervariance on dose specific mean efficacy and toxicity parameters. |
PGLOBAL |
Global monotonicity probability of dose-efficacy curve. |
A list contianing (1) the prior effective sample size, (2) the vector of dose-specific efficacy probability prior mean parameters and, (3) the vector of dose-specific toxicity probability prior mean parameters.
library(mvtnorm) ##Elicited probabilities of toxicity PROBST=c(.05,.10,.15,.20,.30) ##Elicited probabilities of efficacy PROBSE=c(.2,.4,.6,.65,.7) ##Sigma_0 Var=1 ##Sigma_mu HypVar=16 ##Frailty Variance tau=1 ##Global Monotonicity Probability PGLOBAL=.1 Z=GetPriorsSPSO(PROBST,PROBSE,tau,Var,HypVar,PGLOBAL) Zlibrary(mvtnorm) ##Elicited probabilities of toxicity PROBST=c(.05,.10,.15,.20,.30) ##Elicited probabilities of efficacy PROBSE=c(.2,.4,.6,.65,.7) ##Sigma_0 Var=1 ##Sigma_mu HypVar=16 ##Frailty Variance tau=1 ##Global Monotonicity Probability PGLOBAL=.1 Z=GetPriorsSPSO(PROBST,PROBSE,tau,Var,HypVar,PGLOBAL) Z
Provides the optimal dose level as determined by the SPSO model, posterior mean toxicity probability, efficacy probability, and desirability of each dose level. Doses that are unacceptably toxic or inefficous have a desirability of 0.
GetSpsoContour( YE, YT, Doses, Dosetried, cohort, Contour, CutE, CutT, AcceptE, AcceptT, HypermeansE, HypermeansT, Hypervars, B )GetSpsoContour( YE, YT, Doses, Dosetried, cohort, Contour, CutE, CutT, AcceptE, AcceptT, HypermeansE, HypermeansT, Hypervars, B )
YE |
Length n binary indicator vector of efficacy status. |
YT |
Length n binary indicator vector of toxicity status. |
Doses |
Length n vector of integer Doses given to patients. |
Dosetried |
Binary vector corresponding to which doses have been tried. |
cohort |
Cohort size for the trial. |
Contour |
Contour vector for desirability function. Contains in order: (pi_1,E,pi_2,T,pi_3,E,pi_4,T). |
CutE |
Cutoff for efficacy probability acceptability. Dose-efficacy probabilities must be larger than this for patient assignment. |
CutT |
Cutoff for toxicity probability acceptability. Dose-toxicity probabilities must be smaller than this for patient assignment. |
AcceptE |
Posterior probability threshold for efficacy acceptability. |
AcceptT |
Posterior probability threshold for toxicity acceptability. |
HypermeansE |
Hypermeans for dose-specific efficacy parameters. |
HypermeansT |
Hypermeans for dose-specific toxcity parameters. |
Hypervars |
Hypervariances needed for the SPSO model. Contains, in order (sigma_0^2, sigma_mu^2,tau). |
B |
Number of iterations to run for the MCMC. |
A list containing the optimal dose level to assign the next patient cohort in the first entry and a matrix in the second entry, with rows corresponding to (1) the dose #, (2) mean posterior toxicity probability at each dose, mean posterior efficacy probability at each dose, and acceptable posterior desirability score of each dose level (0s indicate the dose is not acceptably efficous or toxic).
##Get the Data ##True Tox Prob PT = c(.1,.2,.25,.5,.7) ##True EFF Prob PE = c(.3,.4,.7,.5,.5) Doses=c(1,1,1,2,2,2,3,3,3,2,2,2,1,1,1,2,2,2,3,3,3) YE=Doses YT=Doses Dosetried=rep(0,length(PE)) ##Generate data for(k in 1:length(PE)){ if(sum(Doses==k)){ ##Dose level has been tried Dosetried[k]=1 YE[Doses==k]=rbinom(sum(Doses==k),1,PE[k]) YT[Doses==k]=rbinom(sum(Doses==k),1,PT[k]) } } ##Hyperparameters HypermeansE=c(-1,-.5,0,.5,1,2) HypermeansT=HypermeansE Hypervars=c(1,16,1) ##Trial parameters cohort=3 ##Contour vector Contour = c(.35, .75,.7,.4) CutE=.3 ##Efficacy threshold CutT=.4 ##Toxicity threshold AcceptE=.3 ##Eff acceptability threshold AcceptT= .3 ##Tox acceptability threshold B=100##Number of iterations GetSpsoContour(YE,YT,Doses,Dosetried,cohort,Contour,CutE, CutT, AcceptE, AcceptT, HypermeansE, HypermeansT,Hypervars,B )##Get the Data ##True Tox Prob PT = c(.1,.2,.25,.5,.7) ##True EFF Prob PE = c(.3,.4,.7,.5,.5) Doses=c(1,1,1,2,2,2,3,3,3,2,2,2,1,1,1,2,2,2,3,3,3) YE=Doses YT=Doses Dosetried=rep(0,length(PE)) ##Generate data for(k in 1:length(PE)){ if(sum(Doses==k)){ ##Dose level has been tried Dosetried[k]=1 YE[Doses==k]=rbinom(sum(Doses==k),1,PE[k]) YT[Doses==k]=rbinom(sum(Doses==k),1,PT[k]) } } ##Hyperparameters HypermeansE=c(-1,-.5,0,.5,1,2) HypermeansT=HypermeansE Hypervars=c(1,16,1) ##Trial parameters cohort=3 ##Contour vector Contour = c(.35, .75,.7,.4) CutE=.3 ##Efficacy threshold CutT=.4 ##Toxicity threshold AcceptE=.3 ##Eff acceptability threshold AcceptT= .3 ##Tox acceptability threshold B=100##Number of iterations GetSpsoContour(YE,YT,Doses,Dosetried,cohort,Contour,CutE, CutT, AcceptE, AcceptT, HypermeansE, HypermeansT,Hypervars,B )
Provides the optimal dose level as determined by the SPSO model, posterior mean toxicity probability, efficacy probability, and mean utility of each dose level. Doses that are unacceptably toxic or inefficous have a utility of 0.
GetSpsoUt( YE, YT, Doses, Dosetried, cohort, UT, CutE, CutT, AcceptE, AcceptT, HypermeansE, HypermeansT, Hypervars, B )GetSpsoUt( YE, YT, Doses, Dosetried, cohort, UT, CutE, CutT, AcceptE, AcceptT, HypermeansE, HypermeansT, Hypervars, B )
YE |
Length n binary indicator vector of efficacy status. |
YT |
Length n binary indicator vector of toxicity status. |
Doses |
Length n vector of integer Doses given to patients. |
Dosetried |
Binary vector corresponding to which doses have been tried. |
cohort |
Cohort size for the trial. |
UT |
Utility matrix for the four bivariate (efficacy, toxicity) events. |
CutE |
Cutoff for efficacy probability acceptability. Dose-efficacy probabilities must be larger than this for patient assignment. |
CutT |
Cutoff for toxicity probability acceptability. Dose-toxicity probabilities must be smaller than this for patient assignment. |
AcceptE |
Posterior probability threshold for efficacy acceptability. |
AcceptT |
Posterior probability threshold for toxicity acceptability. |
HypermeansE |
Hypermeans for dose-specific efficacy parameters. |
HypermeansT |
Hypermeans for dose-specific toxcity parameters. |
Hypervars |
Hypervariances needed for the SPSO model. Contains, in order (sigma_0^2, sigma_mu^2,tau). |
B |
Number of iterations to run for the MCMC. |
A list containing the optimal dose level to assign the next patient cohort in the first entry and a matrix in the second entry, with rows corresponding to (1) the dose #, (2) mean posterior toxicity probability at each dose, mean posterior efficacy probability at each dose, and acceptable posterior utility score of each dose level (0s indicate the dose is not acceptably efficous or toxic).
##Get the Data ##True Tox Prob PT = c(.1,.2,.25,.5,.7) ##True EFF Prob PE = c(.3,.4,.7,.5,.5) Doses=c(1,1,1,2,2,2,3,3,3,2,2,2,1,1,1,2,2,2,3,3,3) YE=Doses YT=Doses Dosetried=rep(0,length(PE)) ##Generate data for(k in 1:length(PE)){ if(sum(Doses==k)){ ##Dose level has been tried Dosetried[k]=1 YE[Doses==k]=rbinom(sum(Doses==k),1,PE[k]) YT[Doses==k]=rbinom(sum(Doses==k),1,PT[k]) } } ##Hyperparameters HypermeansE=c(-1,-.5,0,.5,1,2) HypermeansT=HypermeansE Hypervars=c(1,16,1) ##Trial parameters cohort=3 #'##UTILITY Matrix UT = matrix(c(38.23529,100,0,61.76471),nrow=2,byrow=TRUE) CutE=.3 ##Efficacy threshold CutT=.4 ##Toxicity threshold AcceptE=.3 ##Eff acceptability threshold AcceptT= .3 ##Tox acceptability threshold B=100##Number of iterations GetSpsoUt(YE,YT,Doses,Dosetried,cohort,UT,CutE, CutT, AcceptE, AcceptT, HypermeansE, HypermeansT,Hypervars,B )##Get the Data ##True Tox Prob PT = c(.1,.2,.25,.5,.7) ##True EFF Prob PE = c(.3,.4,.7,.5,.5) Doses=c(1,1,1,2,2,2,3,3,3,2,2,2,1,1,1,2,2,2,3,3,3) YE=Doses YT=Doses Dosetried=rep(0,length(PE)) ##Generate data for(k in 1:length(PE)){ if(sum(Doses==k)){ ##Dose level has been tried Dosetried[k]=1 YE[Doses==k]=rbinom(sum(Doses==k),1,PE[k]) YT[Doses==k]=rbinom(sum(Doses==k),1,PT[k]) } } ##Hyperparameters HypermeansE=c(-1,-.5,0,.5,1,2) HypermeansT=HypermeansE Hypervars=c(1,16,1) ##Trial parameters cohort=3 #'##UTILITY Matrix UT = matrix(c(38.23529,100,0,61.76471),nrow=2,byrow=TRUE) CutE=.3 ##Efficacy threshold CutT=.4 ##Toxicity threshold AcceptE=.3 ##Eff acceptability threshold AcceptT= .3 ##Tox acceptability threshold B=100##Number of iterations GetSpsoUt(YE,YT,Doses,Dosetried,cohort,UT,CutE, CutT, AcceptE, AcceptT, HypermeansE, HypermeansT,Hypervars,B )
Gives the optimal dose level to assign the next patient cohort to based on the vector of optimality function values for each dose level (desirability or utility based) and the doses tried throughout the trial.
ReturnOpt(Desire, DoseTried)ReturnOpt(Desire, DoseTried)
Desire |
Vector of optimality function values for each dose level. |
DoseTried |
Binary vector containing 1 for doses that have been tried during the trial and 0 otherwise. |
Optimal dose level to assign the next patient cohort.
Simulates utility based Efftox trials
RunAdaptiveEffToxTrialCORR( DoseStart, Dose, Hypermeans, Hypervars, UT, PiLim, ProbLim, cohort, NET, NF, B, nSims, PMAT )RunAdaptiveEffToxTrialCORR( DoseStart, Dose, Hypermeans, Hypervars, UT, PiLim, ProbLim, cohort, NET, NF, B, nSims, PMAT )
DoseStart |
Dose level to START the EFF-Tox Trial |
Dose |
Vector of Doses considered in the trial |
Hypermeans |
6 vector of prior means |
Hypervars |
6 vector of prior standard deviations |
UT |
2x2 matrix of utilities corresponding to the four binary bivariate (toxicity, efficacy) events. |
PiLim |
2 vector of acceptable limits |
ProbLim |
2 vector of cutoff for acceptabilities |
cohort |
Cohort size |
NET |
Maximum Sample size to run the EFFtox Trial, must be divisible by cohort |
NF |
Minimum sample size to begin adaptive randomization, must be divisible by count |
B |
Number of reps to perform in MCMC |
nSims |
Number of Simulated trials to run. |
PMAT |
Contains TRUE pi00,pi10, pi01, pi11 for each dose |
Trial simulation results to be processed for operating characteristics summaries.
Simulates Efftox trials based on the desirability tradeoff contour.
RunAdaptiveEffToxTrialCORRCONTOUR( DoseStart, Dose, Hypermeans, Hypervars, Contour, PiLim, ProbLim, cohort, NET, NF, B, nSims, PMAT )RunAdaptiveEffToxTrialCORRCONTOUR( DoseStart, Dose, Hypermeans, Hypervars, Contour, PiLim, ProbLim, cohort, NET, NF, B, nSims, PMAT )
DoseStart |
Dose level to START the EFF-Tox Trial |
Dose |
Vector of Doses considered in the trial |
Hypermeans |
6 vector of prior means |
Hypervars |
6 vector of prior standard deviations |
Contour |
4 vector used to specify the tradeoff contour. |
PiLim |
2 vector of acceptable limits |
ProbLim |
2 vector of cutoff for acceptabilities |
cohort |
Cohort size |
NET |
Maximum Sample size to run the EFFtox Trial, must be divisible by cohort |
NF |
Minimum sample size to begin adaptive randomization, must be divisible by count |
B |
Number of reps to perform in MCMC |
nSims |
Number of Simulated trials to run. |
PMAT |
Contains TRUE pi00,pi10, pi01, pi11 for each dose |
Trial simulation results to be processed for operating characteristics summaries.
Simulates replications from a Bayesian adaptive phase 12 clinical trial design using the Efftox model and a optimality function based on the desirability tradeoff contour.
SimEfftoxContour( NSims, Dose, PE, PT, corET, Nmax, cohort, Contour, CutE, CutT, AcceptE, AcceptT, HypermeansEFF, HypervarsEFF )SimEfftoxContour( NSims, Dose, PE, PT, corET, Nmax, cohort, Contour, CutE, CutT, AcceptE, AcceptT, HypermeansEFF, HypervarsEFF )
NSims |
Number of trial simulations to run. |
Dose |
Logarithm of raw dose levels - the average logarithm of the raw dose levels. |
PE |
True efficacy probability for each dose. |
PT |
True toxicity probaiblity for each dose. |
corET |
Correlation parameter between efficacy and toxicity probability. |
Nmax |
Maximum trial sample size. |
cohort |
Patient cohort size. |
Contour |
Contour vector for desirability function. Contains in order: (pi_1,E,pi_2,T,pi_3,E,pi_4,T). |
CutE |
Cutoff for efficacy probability acceptability. Dose-efficacy probabilities must be larger than this for patient assignment. |
CutT |
Cutoff for toxicity probability acceptability. Dose-toxicity probabilities must be smaller than this for patient assignment. |
AcceptE |
Posterior probability threshold for efficacy acceptability. |
AcceptT |
Posterior probability threshold for toxicity acceptability. |
HypermeansEFF |
Hypermeans for the Efftox model. In order, entries are hypermeans for (beta_0,E,beta_1,E,beta_2,E,beta_0,T,beta_1,T,psi). |
HypervarsEFF |
Hypervariances for the Efftox model. In order, entries are hypervariances for (beta_0,E,beta_1,E,beta_2,E,beta_0,T,beta_1,T,psi). |
A list with the first entry corresponding to a matrix with: (1) True toxicity probabilities at each dose, (2) True efficacy probabilities at each dose, (3) True Desirability of each dose, (4) Optimal dose selection probability, (5) Average sample size of patients treated at each dose. The second entry of the list contains a vector with rows corresponding to (1) the true binary bivariate correlation between efficacy and toxicity, (2) Stopping probability of the trial, (3) Average number of efficacy events, (4) Average number of toxicity events, and (5) Delta.
library(mvtnorm) ##True toxicity probability PT=c(.05,.10,.15,.20,.30) ##True Efficacy Probability PE=c(.2,.4,.6,.65,.7) #True Correlation corET=.5 ##Number of simulations NSims=1 ##Increase this when using ##Hypermeans and hypervariances HypermeansEFF = c(.022,3.45,0,-4.23,3.1,0) HypervarsEFF = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1) HypervarsEFF=HypervarsEFF^2 ##Trial Parameters ##Cohort Size, N^F and N_ET cohort=3 ##Starting Dose DoseStart=1 ##Vector of Numerical Doses Dose = c(1,2,3,3.5,5) ##Dose for Eff-Tox Dose=log(Dose)-mean(log(Dose)) Nmax=30 ##Max Sample Size #Acceptability Criterion CutE=.3 CutT=.4 ##Limits on acceptability AcceptE=.1 AcceptT=.1 ##Contour vector Contour = c(.35, .75,.7,.4) RESULTS=SimEfftoxContour(NSims, Dose,PE, PT, corET, Nmax, cohort, Contour, CutE, CutT, AcceptE,AcceptT, HypermeansEFF, HypervarsEFF) RESULTSlibrary(mvtnorm) ##True toxicity probability PT=c(.05,.10,.15,.20,.30) ##True Efficacy Probability PE=c(.2,.4,.6,.65,.7) #True Correlation corET=.5 ##Number of simulations NSims=1 ##Increase this when using ##Hypermeans and hypervariances HypermeansEFF = c(.022,3.45,0,-4.23,3.1,0) HypervarsEFF = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1) HypervarsEFF=HypervarsEFF^2 ##Trial Parameters ##Cohort Size, N^F and N_ET cohort=3 ##Starting Dose DoseStart=1 ##Vector of Numerical Doses Dose = c(1,2,3,3.5,5) ##Dose for Eff-Tox Dose=log(Dose)-mean(log(Dose)) Nmax=30 ##Max Sample Size #Acceptability Criterion CutE=.3 CutT=.4 ##Limits on acceptability AcceptE=.1 AcceptT=.1 ##Contour vector Contour = c(.35, .75,.7,.4) RESULTS=SimEfftoxContour(NSims, Dose,PE, PT, corET, Nmax, cohort, Contour, CutE, CutT, AcceptE,AcceptT, HypermeansEFF, HypervarsEFF) RESULTS
Simulates replications from a Bayesian adaptive phase 12 clinical trial design using the Efftox model and a optimality function based on mean utility. Does not assign patient cohorts to unacceptably toxic or inefficous dose levels.
SimEfftoxUt( NSims, Dose, PE, PT, corET, Nmax, cohort, UT, CutE, CutT, AcceptE, AcceptT, HypermeansEFF, HypervarsEFF )SimEfftoxUt( NSims, Dose, PE, PT, corET, Nmax, cohort, UT, CutE, CutT, AcceptE, AcceptT, HypermeansEFF, HypervarsEFF )
NSims |
Number of trial simulations to run. |
Dose |
Logarithm of raw dose levels - the average logarithm of the raw dose levels. |
PE |
True efficacy probability for each dose. |
PT |
True toxicity probaiblity for each dose. |
corET |
Correlation parameter between efficacy and toxicity probability. |
Nmax |
Maximum trial sample size. |
cohort |
Patient cohort size. |
UT |
Utility matrix for the four bivariate (efficacy, toxicity) events. |
CutE |
Cutoff for efficacy probability acceptability. Dose-efficacy probabilities must be larger than this for patient assignment. |
CutT |
Cutoff for toxicity probability acceptability. Dose-toxicity probabilities must be smaller than this for patient assignment. |
AcceptE |
Posterior probability threshold for efficacy acceptability. |
AcceptT |
Posterior probability threshold for toxicity acceptability. |
HypermeansEFF |
Hypermeans for the Efftox model. In order, entries are hypermeans for (beta_0,E,beta_1,E,beta_2,E,beta_0,T,beta_1,T,psi). |
HypervarsEFF |
Hypervariances for the Efftox model. In order, entries are hypervariances for (beta_0,E,beta_1,E,beta_2,E,beta_0,T,beta_1,T,psi). |
A list with the first entry corresponding to a matrix with: (1) True toxicity probabilities at each dose, (2) True efficacy probabilities at each dose, (3) True mean utility of each dose, (4) Optimal dose selection probability, (5) Average sample size of patients treated at each dose. The second entry of the list contains a vector with rows corresponding to (1) the true binary bivariate correlation between efficacy and toxicity, (2) Stopping probability of the trial, (3) Average number of efficacy events, (4) Average number of toxicity events, and (5) Delta.
library(mvtnorm) ##True toxicity probability PT=c(.05,.10,.15,.20,.30) ##True Efficacy Probability PE=c(.2,.4,.6,.65,.7) #True Correlation corET=.5 ##Number of simulations NSims=1 ##Increase this when using ##Hypermeans and hypervariances HypermeansEFF = c(.022,3.45,0,-4.23,3.1,0) HypervarsEFF = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1) HypervarsEFF=HypervarsEFF^2 ##Trial Parameters ##Cohort Size, N^F and N_ET cohort=3 ##Starting Dose DoseStart=1 ##Vector of Numerical Doses Dose = c(1,2,3,3.5,5) ##Dose for Eff-Tox Dose=log(Dose)-mean(log(Dose)) Nmax=30 ##Max Sample Size #Acceptability Criterion CutE=.3 CutT=.4 ##Limits on acceptability AcceptE=.1 AcceptT=.1 ##UTILITY Matrix UT = matrix(c(38.23529,100,0,61.76471),nrow=2,byrow=TRUE) RESULTS=SimEfftoxUt(NSims, Dose,PE, PT, corET, Nmax, cohort, UT, CutE, CutT, AcceptE,AcceptT, HypermeansEFF, HypervarsEFF) RESULTSlibrary(mvtnorm) ##True toxicity probability PT=c(.05,.10,.15,.20,.30) ##True Efficacy Probability PE=c(.2,.4,.6,.65,.7) #True Correlation corET=.5 ##Number of simulations NSims=1 ##Increase this when using ##Hypermeans and hypervariances HypermeansEFF = c(.022,3.45,0,-4.23,3.1,0) HypervarsEFF = c(2.6761, 2.6852, .2, 3.1304, 3.1165, 1) HypervarsEFF=HypervarsEFF^2 ##Trial Parameters ##Cohort Size, N^F and N_ET cohort=3 ##Starting Dose DoseStart=1 ##Vector of Numerical Doses Dose = c(1,2,3,3.5,5) ##Dose for Eff-Tox Dose=log(Dose)-mean(log(Dose)) Nmax=30 ##Max Sample Size #Acceptability Criterion CutE=.3 CutT=.4 ##Limits on acceptability AcceptE=.1 AcceptT=.1 ##UTILITY Matrix UT = matrix(c(38.23529,100,0,61.76471),nrow=2,byrow=TRUE) RESULTS=SimEfftoxUt(NSims, Dose,PE, PT, corET, Nmax, cohort, UT, CutE, CutT, AcceptE,AcceptT, HypermeansEFF, HypervarsEFF) RESULTS
Simulates replications from a Bayesian adaptive phase 12 clinical trial design using the SPSO model and a optimality function based on the desirability tradeoff contour.
SimSpsoContour( NSims, PE, PT, corET, Nmax, cohort, Contour, CutE, CutT, AcceptE, AcceptT, HypermeansE, HypermeansT, Hypervars )SimSpsoContour( NSims, PE, PT, corET, Nmax, cohort, Contour, CutE, CutT, AcceptE, AcceptT, HypermeansE, HypermeansT, Hypervars )
NSims |
Number of trial simulations to run. |
PE |
True efficacy probability for each dose. |
PT |
True toxicity probaiblity for each dose. |
corET |
Correlation parameter between efficacy and toxicity probability. |
Nmax |
Maximum trial sample size. |
cohort |
Patient cohort size. |
Contour |
Contour vector for desirability function. Contains in order: (pi_1,E,pi_2,T,pi_3,E,pi_4,T). |
CutE |
Cutoff for efficacy probability acceptability. Dose-efficacy probabilities must be larger than this for patient assignment. |
CutT |
Cutoff for toxicity probability acceptability. Dose-toxicity probabilities must be smaller than this for patient assignment. |
AcceptE |
Posterior probability threshold for efficacy acceptability. |
AcceptT |
Posterior probability threshold for toxicity acceptability. |
HypermeansE |
Hypermeans for dose-specific efficacy parameters. |
HypermeansT |
Hypermeans for dose-specific toxcity parameters. |
Hypervars |
Hypervariances needed for the SPSO model. Contains, in order (sigma_0^2, sigma_mu^2,tau). |
A list with the first entry corresponding to a matrix with: (1) True toxicity probabilities at each dose, (2) True efficacy probabilities at each dose, (3) True Desirability of each dose, (4) Optimal dose selection probability, (5) Average sample size of patients treated at each dose. The second entry of the list contains a vector with rows corresponding to (1) the true binary bivariate correlation between efficacy and toxicity, (2) Stopping probability of the trial, (3) Average number of efficacy events, (4) Average number of toxicity events, and (5) Delta.
library(mvtnorm) ##True toxicity probability PT=c(.05,.10,.15,.20,.30) ##True Efficacy Probability PE=c(.2,.4,.6,.65,.7) #True Correlation corET=.5 ##Number of simulations NSims=1 ##Increase this when using ##Hypermeans and hypervariances HypermeansE=c(-1.189, -0.357, 0.360, 0.546, 0.743) HypermeansT=c(-2.325, -1.811, -1.464, -1.189, -0.740) Hypervars=c(1,16,1) ##Trial Parameters ##Cohort Size, N^F and N_ET cohort=3 ##Starting Dose DoseStart=1 Nmax=30 ##Max Sample Size #Acceptability Criterion CutE=.3 CutT=.4 ##Limits on acceptability AcceptE=.1 AcceptT=.1 ##Contour vector Contour = c(.35, .75,.7,.4) RESULTS=SimSpsoContour(NSims, PE, PT, corET, Nmax, cohort, Contour, CutE, CutT, AcceptE, AcceptT,HypermeansE, HypermeansT, Hypervars) RESULTSlibrary(mvtnorm) ##True toxicity probability PT=c(.05,.10,.15,.20,.30) ##True Efficacy Probability PE=c(.2,.4,.6,.65,.7) #True Correlation corET=.5 ##Number of simulations NSims=1 ##Increase this when using ##Hypermeans and hypervariances HypermeansE=c(-1.189, -0.357, 0.360, 0.546, 0.743) HypermeansT=c(-2.325, -1.811, -1.464, -1.189, -0.740) Hypervars=c(1,16,1) ##Trial Parameters ##Cohort Size, N^F and N_ET cohort=3 ##Starting Dose DoseStart=1 Nmax=30 ##Max Sample Size #Acceptability Criterion CutE=.3 CutT=.4 ##Limits on acceptability AcceptE=.1 AcceptT=.1 ##Contour vector Contour = c(.35, .75,.7,.4) RESULTS=SimSpsoContour(NSims, PE, PT, corET, Nmax, cohort, Contour, CutE, CutT, AcceptE, AcceptT,HypermeansE, HypermeansT, Hypervars) RESULTS
Simulates replications from a Bayesian adaptive phase 12 clinical trial design using the SPSO model and a optimality function based on the desirability tradeoff contour.
SimSpsoUt( NSims, PE, PT, corET, Nmax, cohort, UT, CutE, CutT, AcceptE, AcceptT, HypermeansE, HypermeansT, Hypervars )SimSpsoUt( NSims, PE, PT, corET, Nmax, cohort, UT, CutE, CutT, AcceptE, AcceptT, HypermeansE, HypermeansT, Hypervars )
NSims |
Number of trial simulations to run. |
PE |
True efficacy probability for each dose. |
PT |
True toxicity probaiblity for each dose. |
corET |
Correlation parameter between efficacy and toxicity probability. |
Nmax |
Maximum trial sample size. |
cohort |
Patient cohort size. |
UT |
Utility matrix for the four bivariate (efficacy, toxicity) events. |
CutE |
Cutoff for efficacy probability acceptability. Dose-efficacy probabilities must be larger than this for patient assignment. |
CutT |
Cutoff for toxicity probability acceptability. Dose-toxicity probabilities must be smaller than this for patient assignment. |
AcceptE |
Posterior probability threshold for efficacy acceptability. |
AcceptT |
Posterior probability threshold for toxicity acceptability. |
HypermeansE |
Hypermeans for dose-specific efficacy parameters. |
HypermeansT |
Hypermeans for dose-specific toxcity parameters. |
Hypervars |
Hypervariances needed for the SPSO model. Contains, in order (sigma_0^2, sigma_mu^2,tau). |
A list with the first entry corresponding to a matrix with: (1) True toxicity probabilities at each dose, (2) True efficacy probabilities at each dose, (3) True mean utility of each dose, (4) Optimal dose selection probability, (5) Average sample size of patients treated at each dose. The second entry of the list contains a vector with rows corresponding to (1) the true binary bivariate correlation between efficacy and toxicity, (2) Stopping probability of the trial, (3) Average number of efficacy events, (4) Average number of toxicity events, and (5) Delta.
library(mvtnorm) ##True toxicity probability PT=c(.05,.10,.15,.20,.30) ##True Efficacy Probability PE=c(.2,.4,.6,.65,.7) #True Correlation corET=.5 ##Number of simulations NSims=1 ##Increase this when using ##Hypermeans and hypervariances HypermeansE=c(-1.189, -0.357, 0.360, 0.546, 0.743) HypermeansT=c(-2.325, -1.811, -1.464, -1.189, -0.740) Hypervars=c(1,16,1) ##Trial Parameters ##Cohort Size, N^F and N_ET cohort=3 ##Starting Dose DoseStart=1 Nmax=30 ##Max Sample Size #Acceptability Criterion CutE=.3 CutT=.4 ##Limits on acceptability AcceptE=.1 AcceptT=.1 ##UTILITY Matrix UT = matrix(c(38.23529,100,0,61.76471),nrow=2,byrow=TRUE) RESULTS=SimSpsoUt(NSims, PE, PT, corET, Nmax, cohort, UT, CutE, CutT, AcceptE, AcceptT,HypermeansE, HypermeansT, Hypervars) RESULTSlibrary(mvtnorm) ##True toxicity probability PT=c(.05,.10,.15,.20,.30) ##True Efficacy Probability PE=c(.2,.4,.6,.65,.7) #True Correlation corET=.5 ##Number of simulations NSims=1 ##Increase this when using ##Hypermeans and hypervariances HypermeansE=c(-1.189, -0.357, 0.360, 0.546, 0.743) HypermeansT=c(-2.325, -1.811, -1.464, -1.189, -0.740) Hypervars=c(1,16,1) ##Trial Parameters ##Cohort Size, N^F and N_ET cohort=3 ##Starting Dose DoseStart=1 Nmax=30 ##Max Sample Size #Acceptability Criterion CutE=.3 CutT=.4 ##Limits on acceptability AcceptE=.1 AcceptT=.1 ##UTILITY Matrix UT = matrix(c(38.23529,100,0,61.76471),nrow=2,byrow=TRUE) RESULTS=SimSpsoUt(NSims, PE, PT, corET, Nmax, cohort, UT, CutE, CutT, AcceptE, AcceptT,HypermeansE, HypermeansT, Hypervars) RESULTS
Takes arguments of data, hypermens and hypervariance vectors and returns a list of posterior samples from the Utility based phase12 model decribed by Chapple and Thall (2019).
UTEFFTOX(YE, YT, Doses, HypermeansEFF, HypermeansTOX, Hypervars, B)UTEFFTOX(YE, YT, Doses, HypermeansEFF, HypermeansTOX, Hypervars, B)
YE |
Binary indicator vector of efficacy status. |
YT |
Binary indicator vector of toxicity status. |
Doses |
Vector of integer Doses given to patients. |
HypermeansEFF |
Vector of length nDose for dose prior means for efficacy. |
HypermeansTOX |
Vector of length nDose for dose prior means for toxicity |
Hypervars |
Length 3 vector of hypervariances.Hypervars(1) contains sigma_0^2, Hypervars(2) contains sigma_mu, Hypervars(3) contains tau - the frailty variance. |
B |
Number of iterations to run for the MCMC. |
A list of posterior samples after burnin in order: Posterior efficacy dose-vector, Posterior toxicity dose-vector, Posterior correlation.
n=100 #Generate Data YE=rbinom(n,1,.6) YT=rbinom(n,1,.2) nDose=5 Doses=sample(1:nDose,n,replace=TRUE) ##Hyperparameters HypermeansEFF=c(-1,-.5,0,.5,1,2) HypermeansTOX=HypermeansEFF Hypervars=c(1,16,1) B=100 UTEFFTOX(YE, YT,Doses,HypermeansEFF,HypermeansTOX, Hypervars, B)n=100 #Generate Data YE=rbinom(n,1,.6) YT=rbinom(n,1,.2) nDose=5 Doses=sample(1:nDose,n,replace=TRUE) ##Hyperparameters HypermeansEFF=c(-1,-.5,0,.5,1,2) HypermeansTOX=HypermeansEFF Hypervars=c(1,16,1) B=100 UTEFFTOX(YE, YT,Doses,HypermeansEFF,HypermeansTOX, Hypervars, B)