Object-Oriented Implementation of CRM Designs


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Documentation for package ‘crmPack’ version 1.0.6

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crmPack-package Object-oriented implementation of CRM designs

-- A --

AllModels-class Class for All models This is a class where all models inherit.
approximate Approximate posterior with (log) normal distribution
approximate-method Approximate posterior with (log) normal distribution
as.list-method as.list method for the "GeneralData" class

-- B --

biomLevel Compute the biomarker level for a given dose, given model and samples
biomLevel-method Compute the biomarker level for a given dose, given model and samples

-- C --

CohortSize-class The virtual class for cohort sizes
CohortSizeConst Initialization function for "CohortSizeConst"
CohortSizeConst-class Constant cohort size
CohortSizeDLT Initialization function for "CohortSizeDLT"
CohortSizeDLT-class Cohort size based on number of DLTs
CohortSizeMax Initialization function for "CohortSizeMax"
CohortSizeMax-class Size based on maximum of multiple cohort size rules
CohortSizeMin Initialization function for "CohortSizeMin"
CohortSizeMin-class Size based on minimum of multiple cohort size rules
CohortSizeParts Initialization function for "CohortSizeParts"
CohortSizeParts-class Cohort size based on the parts
CohortSizeRange Initialization function for "CohortSizeRange"
CohortSizeRange-class Cohort size based on dose range
crmPack Object-oriented implementation of CRM designs
crmPackExample Open the example pdf for crmPack
crmPackHelp Open the browser with help pages for crmPack

-- D --

Data Initialization function for the "Data" class
Data-class Class for the data input
DataDual Initialization function for the "DataDual" class
DataDual-class Class for the dual endpoint data input
DataMixture Initialization function for the "DataMixture" class
DataMixture-class Class for the data with mixture sharing
DataParts Initialization function for the "DataParts" class
DataParts-class Class for the data with two study parts
Design Initialization function for "Design"
Design-class Class for the CRM design
dose Compute the doses for a given probability, given model and samples
dose-method Compute the doses for a given probability, given model and samples
DualDesign Initialization function for "DualDesign"
DualDesign-class Class for the dual-endpoint CRM design
DualEndpoint Initialization function for the "DualEndpoint" class
DualEndpoint-class General class for the dual endpoint model
DualEndpointBeta Initialization function for the "DualEndpointBeta" class
DualEndpointBeta-class Dual endpoint model with beta function for dose-biomarker relationship
DualEndpointEmax Initialization function for the "DualEndpointEmax" class
DualEndpointEmax-class Dual endpoint model with emax function for dose-biomarker relationship
DualEndpointRW Initialization function for the "DualEndpointRW" class
DualEndpointRW-class Dual endpoint model with RW prior for biomarker
DualResponsesDesign Initialization function for 'DualResponsesDesign"
DualResponsesDesign-class This is a class of design based on DLE responses using the 'LogisticIndepBeta' model model and efficacy responses using 'ModelEff' model class without DLE and efficacy samples. It contain all slots in 'RuleDesign' and 'TDDesign' class object
DualResponsesSamplesDesign Initialization function for 'DualResponsesSamplesDesign"
DualResponsesSamplesDesign-class This is a class of design based on DLE responses using the 'LogisticIndepBeta' model model and efficacy responses using 'ModelEff' model class with DLE and efficacy samples.It contain all slots in 'RuleDesign' and 'TDsamplesDesign' class object
DualSimulations Initialization function for "DualSimulations"
DualSimulations-class Class for the simulations output from dual-endpoint model based designs
DualSimulationsSummary-class Class for the summary of dual-endpoint simulations output

-- E --

EffFlexi Initialization function for the "EffFlexi" class
EffFlexi-class Class for the efficacy model in flexible form for prior expressed in form of pseudo data
Effloglog Initialization function for the "Effloglog" class
Effloglog-class Class for the linear log-log efficacy model using pseudo data prior
examine Obtain hypothetical trial course table for a design
examine-method Obtain hypothetical trial course table for a design
ExpEff Compute the expected efficacy based on a given dose, a given pseudo Efficacy log-log model and a given efficacy sample
ExpEff-method Compute the expected efficacy based on a given dose, a given pseudo Efficacy log-log model and a given efficacy sample

-- F --

fit Fit method for the Samples class
fit-method Fit method for the Samples class
fitGain Get the fiited values for the gain values at all dose levels based on a given pseudo DLE model, DLE sample, a pseudo efficacy model, a Efficacy sample and data. This method returns a data frame with dose, middle, lower and upper quantiles of the gain value samples
fitGain-method Get the fiited values for the gain values at all dose levels based on a given pseudo DLE model, DLE sample, a pseudo efficacy model, a Efficacy sample and data. This method returns a data frame with dose, middle, lower and upper quantiles of the gain value samples

-- G --

gain Compute the gain value with a given dose level, given a pseudo DLE model, a DLE sample, a pseudo Efficacy log-log model and a Efficacy sample
gain-method Compute the gain value with a given dose level, given a pseudo DLE model, a DLE sample, a pseudo Efficacy log-log model and a Efficacy sample
GeneralData-class Class for general data input
GeneralModel-class No Intitialization function for this General class for model input
GeneralSimulations Initialization function for "GeneralSimulations"
GeneralSimulations-class General class for the simulations output
GeneralSimulationsSummary-class Class for the summary of general simulations output
get-method Get specific parameter samples and produce a data.frame
getEff Extracting efficacy responses for subjects without or with a DLE. This is a class where we separate efficacy responses with or without a DLE. It outputs the efficacy responses and their corresponding dose levels treated at in two categories (with or without DLE)
getEff-method Extracting efficacy responses for subjects without or with a DLE. This is a class where we separate efficacy responses with or without a DLE. It outputs the efficacy responses and their corresponding dose levels treated at in two categories (with or without DLE)
getMinInfBeta Get the minimal informative unimodal beta distribution

-- I --

IncrementMin Initialization function for "IncrementMin"
IncrementMin-class Max increment based on minimum of multiple increment rules
Increments-class The virtual class for controlling increments
IncrementsNumDoseLevels Initialization function for "IncrementsNumDoseLevels"
IncrementsNumDoseLevels-class Increments control based on number of dose levels
IncrementsRelative Initialization function for "IncrementsRelative"
IncrementsRelative-class Increments control based on relative differences in intervals
IncrementsRelativeDLT Initialization function for "IncrementsRelativeDLT"
IncrementsRelativeDLT-class Increments control based on relative differences in terms of DLTs
IncrementsRelativeParts Initialization function for "IncrementsRelativeParts"
IncrementsRelativeParts-class Increments control based on relative differences in intervals, with special rules for part 1 and beginning of part 2
initialize-method Initialization method for the "DualEndpointOld" class

-- L --

LogisticIndepBeta Intialization function for "LogisticIndepBeta" class
LogisticIndepBeta-class No initialization function Standard logistic model with prior in form of pseudo data
LogisticKadane Initialization function for the "LogisticKadane" class
LogisticKadane-class Reparametrized logistic model
LogisticLogNormal Initialization function for the "LogisticLogNormal" class
LogisticLogNormal-class Standard logistic model with bivariate (log) normal prior
LogisticLogNormalMixture Initialization function for the "LogisticLogNormalMixture" class
LogisticLogNormalMixture-class Standard logistic model with online mixture of two bivariate log normal priors
LogisticLogNormalSub Initialization function for the "LogisticLogNormalSub" class
LogisticLogNormalSub-class Standard logistic model with bivariate (log) normal prior with substractive dose standardization
LogisticNormal Initialization function for the "LogisticNormal" class
LogisticNormal-class Standard logistic model with bivariate normal prior
LogisticNormalFixedMixture Initialization function for the "LogisticNormalFixedMixture" class
LogisticNormalFixedMixture-class Standard logistic model with fixed mixture of multiple bivariate (log) normal priors
LogisticNormalMixture Initialization function for the "LogisticNormalMixture" class
LogisticNormalMixture-class Standard logistic model with flexible mixture of two bivariate normal priors
logit Shorthand for logit function

-- M --

matchTolerance Helper function for value matching with tolerance
maxDose Determine the maximum possible next dose
maxDose-method Determine the maximum possible next dose
maxSize "MAX" combination of cohort size rules
maxSize-method "MAX" combination of cohort size rules
mcmc Obtain posterior samples for all model parameters
mcmc-method Obtain posterior samples for all model parameters
McmcOptions Initialization function for the "McmcOptions" class
McmcOptions-class Class for the three canonical MCMC options
MinimalInformative Construct a minimally informative prior
minSize "MIN" combination of cohort size rules
minSize-method "MIN" combination of cohort size rules
Model-class Class for the model input
ModelEff-class No Initialization function class for Efficacy models using pseudo data prior
ModelPseudo-class Class of models using expressing their prior in form of Pseudo data
ModelTox-class No intialization function Class for DLE models using pseudo data prior. This is a class of DLE (dose-limiting events) models/ toxicity model which contains all DLE models for which their prior are specified in form of pseudo data (as if there is some data before the trial starts). It inherits all slots from 'ModelPseudo'
multiplot Multiple plot function

-- N --

nextBest Find the next best dose
NextBest-class The virtual class for finding next best dose
nextBest-method Find the next best dose
NextBestDualEndpoint Initialization function for "NextBestDualEndpoint"
NextBestDualEndpoint-class The class with the input for finding the next dose based on the dual endpoint model
NextBestMaxGain Initialization function for the class 'NextBestMaxGain'
NextBestMaxGain-class Next best dose with maximum gain value based on a pseudo DLE and efficacy model without samples
NextBestMaxGainSamples Initialization function for class "NextBestMaxGainSamples"
NextBestMaxGainSamples-class Next best dose with maximum gain value based on a pseudo DLE and efficacy model with samples
NextBestMTD Initialization function for class "NextBestMTD"
NextBestMTD-class The class with the input for finding the next best MTD estimate
NextBestNCRM Initialization function for "NextBestNCRM"
NextBestNCRM-class The class with the input for finding the next dose in target interval
NextBestTD Initialization function for the class "NextBestTD"
NextBestTD-class Next best dose based on Pseudo DLE model without sample
NextBestTDsamples Initialization function for class "NextBestTDsamples"
NextBestTDsamples-class Next best dose based on Pseudo DLE Model with samples
NextBestThreePlusThree Initialization function for "NextBestThreePlusThree"
NextBestThreePlusThree-class The class with the input for finding the next dose in target interval

-- O --

or-Stopping-Stopping The method combining two atomic stopping rules
or-Stopping-StoppingAny The method combining a stopping list and an atomic
or-StoppingAny-Stopping The method combining an atomic and a stopping list

-- P --

plot-method Plot of the fitted dose-tox based with a given pseudo DLE model and data without samples
plot-method Plot method for the "Data" class
plot-method Plot of the fitted dose-efficacy based with a given pseudo efficacy model and data without samples
plot-method Plot method for the "DataDual" class
plot-method Plot dual-endpoint simulations
plot-method Plot summaries of the dual-endpoint design simulations
plot-method Plot simulations
plot-method Graphical display of the general simulation summary
plot-method Plot for PseudoDualFlexiSimulations
plot-method Plot simulations
plot-method Plot the summary of Pseudo Dual Simulations summary
plot-method Plot summaries of the pseudo simulations
plot-method Plotting dose-toxicity and dose-biomarker model fits
plot-method Plotting dose-toxicity model fits
plot-method Plot the fitted dose-effcacy curve using a model from 'ModelEff' class with samples
plot-method Plot the fitted dose-DLE curve using a 'ModelTox' class model with samples
plot-method Plot summaries of the model-based design simulations
plot.gtable Plots gtable objects
plotDualResponses Plot of the DLE and efficacy curve side by side given a DLE pseudo model, a DLE sample, an efficacy pseudo model and a given efficacy sample
plotDualResponses-method Plot of the DLE and efficacy curve side by side given a DLE pseudo model, a DLE sample, an efficacy pseudo model and a given efficacy sample
plotGain Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model, a DLE sample, a given efficacy pseudo model and an efficacy sample
plotGain-method Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model, a DLE sample, a given efficacy pseudo model and an efficacy sample
prob Compute the probability for a given dose, given model and samples
prob-method Compute the probability for a given dose, given model and samples
probit Shorthand for probit function
ProbitLogNormal Initialization function for the "ProbitLogNormal" class
ProbitLogNormal-class Probit model with bivariate log normal prior
PseudoDualFlexiSimulations Initialization function for 'PseudoDualFlexiSimulations' class
PseudoDualFlexiSimulations-class This is a class which captures the trial simulations design using both the DLE and efficacy responses. The design of model from 'ModelTox' class and the efficacy model from 'EffFlexi' class It contains all slots from 'GeneralSimulations', 'PseudoSimulations' and 'PseudoDualSimulations' object. In comparison to the parent class 'PseudoDualSimulations', it contains additional slots to capture the sigma2betaW estimates.
PseudoDualSimulations Initialization function for 'DualPseudoSimulations' class
PseudoDualSimulations-class This is a class which captures the trial simulations design using both the DLE and efficacy responses. The design of model from 'ModelTox' class and the efficacy model from 'ModelEff' class (except 'EffFlexi' class). It contains all slots from 'GeneralSimulations' and 'PseudoSimulations' object. In comparison to the parent class 'PseudoSimulations', it contains additional slots to capture the dose-efficacy curve and the sigma2 estimates.
PseudoDualSimulationsSummary-class Class for the summary of the dual responses simulations using pseudo models
PseudoSimulations Initialization function of the 'PseudoSimulations' class
PseudoSimulations-class This is a class which captures the trial simulations from designs using pseudo model. The design for DLE only responses and model from 'ModelTox' class object. It contains all slots from 'GeneralSimulations' object. Additional slots fit and stopReasons compared to the general class 'GeneralSimulations'.
PseudoSimulationsSummary-class Class for the summary of pseudo-models simulations output

-- Q --

Quantiles2LogisticNormal Convert prior quantiles (lower, median, upper) to logistic (log) normal model

-- R --

Report A Reference Class to represent sequentially updated reporting objects.
RuleDesign Initialization function for "RuleDesign"
RuleDesign-class Class for rule-based designs

-- S --

Samples Initialization function for "Samples"
Samples-class Class for the MCMC output
sampleSize Compute the number of samples for a given MCMC options triple
setSeed Helper function to set and save the RNG seed
show-method Show the summary of the dual-endpoint simulations
show-method Show the summary of the simulations
show-method Show the summary of Pseudo Dual simulations summary
show-method Show the summary of the simulations
show-method Show the summary of the simulations
simulate-method Simulate outcomes from a CRM design
simulate-method Simulate outcomes from a dual-endpoint design
simulate-method This is a methods to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the 'DualResponsesDesign' where DLEmodel used are of 'ModelTox' class object and efficacy model used are of 'ModelEff' class object. In addition, no DLE and efficacy samples are involved or generated in the simulation process
simulate-method This is a methods to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the 'DualResponsesSamplesDesign' where DLEmodel used are of 'ModelTox' class object and efficacy model used are of 'ModelEff' class object (special case is 'EffFlexi' class model object). In addition, DLE and efficacy samples are involved or generated in the simulation process
simulate-method Simulate outcomes from a rule-based design
simulate-method This is a methods to simulate dose escalation procedure only using the DLE responses. This is a method based on the 'TDDesign' where model used are of 'ModelTox' class object and no samples are involved.
simulate-method This is a methods to simulate dose escalation procedure only using the DLE responses. This is a method based on the 'TDsamplesDesign' where model used are of 'ModelTox' class object DLE samples are also used
Simulations Initialization function for the "Simulations" class
Simulations-class Class for the simulations output from model based designs
SimulationsSummary-class Class for the summary of model-based simulations output
size Determine the size of the next cohort
size-method Determine the size of the next cohort
Stopping-class The virtual class for stopping rules
StoppingAll Initialization function for "StoppingAll"
StoppingAll-class Stop based on fullfillment of all multiple stopping rules
StoppingAny Initialization function for "StoppingAny"
StoppingAny-class Stop based on fullfillment of any stopping rule
StoppingCohortsNearDose Initialization function for "StoppingCohortsNearDose"
StoppingCohortsNearDose-class Stop based on number of cohorts near to next best dose
StoppingGstarCIRatio Initialization function for "StoppingGstarCIRatio"
StoppingGstarCIRatio-class Stop based on a target ratio, the ratio of the upper to the lower 95% credibility interval of the estimate of the minimum of the dose which gives the maximum gain (Gstar) and the TD end of trial, the dose with probability of DLE equals to the target probability of DLE used at the end of a trial.
StoppingHighestDose Initialization function for "StoppingHighestDose"
StoppingHighestDose-class Stop when the highest dose is reached
StoppingList Initialization function for "StoppingList"
StoppingList-class Stop based on multiple stopping rules
StoppingMinCohorts Initialization function for "StoppingMinCohorts"
StoppingMinCohorts-class Stop based on minimum number of cohorts
StoppingMinPatients Initialization function for "StoppingMinPatients"
StoppingMinPatients-class Stop based on minimum number of patients
StoppingMTDdistribution Initialization function for "StoppingMTDdistribution"
StoppingMTDdistribution-class Stop based on MTD distribution
StoppingPatientsNearDose Initialization function for "StoppingPatientsNearDose"
StoppingPatientsNearDose-class Stop based on number of patients near to next best dose
StoppingTargetBiomarker Initialization function for "StoppingTargetBiomarker"
StoppingTargetBiomarker-class Stop based on probability of target biomarker
StoppingTargetProb Initialization function for "StoppingTargetProb"
StoppingTargetProb-class Stop based on probability of target tox interval
StoppingTDCIRatio Initialization function for "StoppingTDCIRatio"
StoppingTDCIRatio-class Stop based on a target ratio, the ratio of the upper to the lower 95% credibility interval of the estimate of TD end of trial, the dose with probability of DLE equals to the target probability of DLE used at the end of a trial
stopTrial Stop the trial?
stopTrial-method Stop the trial?
summary-method Summarize the dual-endpoint design simulations, relative to given true dose-toxicity and dose-biomarker curves
summary-method Summarize the simulations, relative to a given truth
summary-method Summary for Pseudo Dual responses simulations given a pseudo DLE model and the Flexible efficacy model.
summary-method Summary for Pseudo Dual responses simulations, relative to a given pseudo DLE and efficacy model (except the EffFlexi class model)
summary-method Summarize the simulations, relative to a given truth
summary-method Summarize the model-based design simulations, relative to a given truth

-- T --

TDDesign Initialization function for 'TDDesign' class
TDDesign-class Design class using DLE responses only based on the pseudo DLE model without sample
TDsamplesDesign Initialization function for 'TDsamplesDesign' class
TDsamplesDesign-class This is a class of design based only on DLE responses using the 'LogisticIndepBeta' class model and DLE samples are also used. In addition to the slots in the more simple 'RuleDesign', objects of this class contain:
ThreePlusThreeDesign Creates a new 3+3 design object from a dose grid

-- U --

update-method Update method for the "Data" class
update-method Update method for the "DataDual" class
update-method Update method for the "DataParts" class
update-method Update method for the 'EffFlexi' Model class. This is a method to update estimates both for the flexible form model and the random walk model (see details in 'EffFlexi' class object) when new data or new observations of responses are available and added in.
update-method Update method for the 'Effloglog' Model class. This is a method to update the modal estimates of the model parameters theta_1 (theta1), theta_2 (theta2) and nu (nu, the precision of the efficacy responses) when new data or new observations of responses are available and added in.
update-method Update method for the 'LogisticIndepBeta'Model class. This is a method to update the modal estimates of the model parameters phi_1 (phi1) and phi_2 (phi2) when new data or new observations of responses are available and added in.

-- V --

Validate A Reference Class to help programming validation for new S4 classes

-- W --

writeModel Creating a WinBUGS model file

-- misc --

%~% Helper function for value matching with tolerance
&-method The method combining two atomic stopping rules
&-method The method combining an atomic and a stopping list
&-method The method combining a stopping list and an atomic
.AllModels Class for All models This is a class where all models inherit.
.CohortSizeConst Constant cohort size
.CohortSizeDLT Cohort size based on number of DLTs
.CohortSizeMax Size based on maximum of multiple cohort size rules
.CohortSizeMin Size based on minimum of multiple cohort size rules
.CohortSizeParts Cohort size based on the parts
.CohortSizeRange Cohort size based on dose range
.Data Class for the data input
.DataDual Class for the dual endpoint data input
.DataMixture Class for the data with mixture sharing
.DataParts Class for the data with two study parts
.Design Class for the CRM design
.DualDesign Class for the dual-endpoint CRM design
.DualEndpoint General class for the dual endpoint model
.DualEndpointBeta Dual endpoint model with beta function for dose-biomarker relationship
.DualEndpointEmax Dual endpoint model with emax function for dose-biomarker relationship
.DualEndpointRW Dual endpoint model with RW prior for biomarker
.DualResponsesDesign This is a class of design based on DLE responses using the 'LogisticIndepBeta' model model and efficacy responses using 'ModelEff' model class without DLE and efficacy samples. It contain all slots in 'RuleDesign' and 'TDDesign' class object
.DualResponsesSamplesDesign This is a class of design based on DLE responses using the 'LogisticIndepBeta' model model and efficacy responses using 'ModelEff' model class with DLE and efficacy samples.It contain all slots in 'RuleDesign' and 'TDsamplesDesign' class object
.DualSimulations Class for the simulations output from dual-endpoint model based designs
.DualSimulationsSummary Class for the summary of dual-endpoint simulations output
.EffFlexi Class for the efficacy model in flexible form for prior expressed in form of pseudo data
.Effloglog Class for the linear log-log efficacy model using pseudo data prior
.GeneralData Class for general data input
.GeneralModel No Intitialization function for this General class for model input
.GeneralSimulations General class for the simulations output
.GeneralSimulationsSummary Class for the summary of general simulations output
.IncrementMin Max increment based on minimum of multiple increment rules
.IncrementsNumDoseLevels Increments control based on number of dose levels
.IncrementsRelative Increments control based on relative differences in intervals
.IncrementsRelativeDLT Increments control based on relative differences in terms of DLTs
.IncrementsRelativeParts Increments control based on relative differences in intervals, with special rules for part 1 and beginning of part 2
.LogisticIndepBeta No initialization function Standard logistic model with prior in form of pseudo data
.LogisticKadane Reparametrized logistic model
.LogisticLogNormal Standard logistic model with bivariate (log) normal prior
.LogisticLogNormalMixture Standard logistic model with online mixture of two bivariate log normal priors
.LogisticLogNormalSub Standard logistic model with bivariate (log) normal prior with substractive dose standardization
.LogisticNormal Standard logistic model with bivariate normal prior
.LogisticNormalFixedMixture Standard logistic model with fixed mixture of multiple bivariate (log) normal priors
.LogisticNormalMixture Standard logistic model with flexible mixture of two bivariate normal priors
.McmcOptions Class for the three canonical MCMC options
.Model Class for the model input
.ModelEff No Initialization function class for Efficacy models using pseudo data prior
.ModelPseudo Class of models using expressing their prior in form of Pseudo data
.ModelTox No intialization function Class for DLE models using pseudo data prior. This is a class of DLE (dose-limiting events) models/ toxicity model which contains all DLE models for which their prior are specified in form of pseudo data (as if there is some data before the trial starts). It inherits all slots from 'ModelPseudo'
.NextBestDualEndpoint The class with the input for finding the next dose based on the dual endpoint model
.NextBestMaxGain Next best dose with maximum gain value based on a pseudo DLE and efficacy model without samples
.NextBestMaxGainSamples Next best dose with maximum gain value based on a pseudo DLE and efficacy model with samples
.NextBestMTD The class with the input for finding the next best MTD estimate
.NextBestNCRM The class with the input for finding the next dose in target interval
.NextBestTD Next best dose based on Pseudo DLE model without sample
.NextBestTDsamples Next best dose based on Pseudo DLE Model with samples
.NextBestThreePlusThree The class with the input for finding the next dose in target interval
.ProbitLogNormal Probit model with bivariate log normal prior
.PseudoDualFlexiSimulations This is a class which captures the trial simulations design using both the DLE and efficacy responses. The design of model from 'ModelTox' class and the efficacy model from 'EffFlexi' class It contains all slots from 'GeneralSimulations', 'PseudoSimulations' and 'PseudoDualSimulations' object. In comparison to the parent class 'PseudoDualSimulations', it contains additional slots to capture the sigma2betaW estimates.
.PseudoDualSimulations This is a class which captures the trial simulations design using both the DLE and efficacy responses. The design of model from 'ModelTox' class and the efficacy model from 'ModelEff' class (except 'EffFlexi' class). It contains all slots from 'GeneralSimulations' and 'PseudoSimulations' object. In comparison to the parent class 'PseudoSimulations', it contains additional slots to capture the dose-efficacy curve and the sigma2 estimates.
.PseudoDualSimulationsSummary Class for the summary of the dual responses simulations using pseudo models
.PseudoSimulations This is a class which captures the trial simulations from designs using pseudo model. The design for DLE only responses and model from 'ModelTox' class object. It contains all slots from 'GeneralSimulations' object. Additional slots fit and stopReasons compared to the general class 'GeneralSimulations'.
.PseudoSimulationsSummary Class for the summary of pseudo-models simulations output
.RuleDesign Class for rule-based designs
.Samples Class for the MCMC output
.Simulations Class for the simulations output from model based designs
.SimulationsSummary Class for the summary of model-based simulations output
.StoppingAll Stop based on fullfillment of all multiple stopping rules
.StoppingAny Stop based on fullfillment of any stopping rule
.StoppingCohortsNearDose Stop based on number of cohorts near to next best dose
.StoppingGstarCIRatio Stop based on a target ratio, the ratio of the upper to the lower 95% credibility interval of the estimate of the minimum of the dose which gives the maximum gain (Gstar) and the TD end of trial, the dose with probability of DLE equals to the target probability of DLE used at the end of a trial.
.StoppingHighestDose Stop when the highest dose is reached
.StoppingList Stop based on multiple stopping rules
.StoppingMinCohorts Stop based on minimum number of cohorts
.StoppingMinPatients Stop based on minimum number of patients
.StoppingMTDdistribution Stop based on MTD distribution
.StoppingPatientsNearDose Stop based on number of patients near to next best dose
.StoppingTargetBiomarker Stop based on probability of target biomarker
.StoppingTargetProb Stop based on probability of target tox interval
.StoppingTDCIRatio Stop based on a target ratio, the ratio of the upper to the lower 95% credibility interval of the estimate of TD end of trial, the dose with probability of DLE equals to the target probability of DLE used at the end of a trial
.TDDesign Design class using DLE responses only based on the pseudo DLE model without sample
.TDsamplesDesign This is a class of design based only on DLE responses using the 'LogisticIndepBeta' class model and DLE samples are also used. In addition to the slots in the more simple 'RuleDesign', objects of this class contain:
|-method The method combining two atomic stopping rules
|-method The method combining a stopping list and an atomic
|-method The method combining an atomic and a stopping list