R: Object-Oriented Implementation of CRM Designs

# Object-Oriented Implementation of CRM Designs

## Documentation for package ‘crmPack’ version 1.0.5

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A B C D E F G I L M N O P Q R S T U V W misc

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