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 |
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 |
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 |
%~% |
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 |