crmPack

Class for All models This is a class where all models inherit.

The virtual class for cohort sizes

Initialization function for "CohortSizeConst"

Constant cohort size

Initialization function for "CohortSizeDLT"

Cohort size based on number of DLTs

Initialization function for "CohortSizeMax"

Size based on maximum of multiple cohort size rules

Initialization function for "CohortSizeMin"

Size based on minimum of multiple cohort size rules

Initialization function for "CohortSizeParts"

Cohort size based on the parts

Initialization function for "CohortSizeRange"

Cohort size based on dose range

Initialization function for the "Data" class

Class for the data input

Initialization function for the "DataDual" class

Class for the dual endpoint data input

Initialization function for the "DataMixture" class

Class for the data with mixture sharing

Initialization function for the "DataParts" class

Class for the data with two study parts

Initialization function for "Design"

Class for the CRM design

Initialization function for "DualDesign"

Class for the dual-endpoint CRM design

Initialization function for the "DualEndpoint" class

General class for the dual endpoint model

Initialization function for the "DualEndpointBeta" class

Dual endpoint model with beta function for dose-biomarker relationship

Initialization function for the "DualEndpointEmax" class

Dual endpoint model with emax function for dose-biomarker relationship

Dual endpoint model

Initialization function for the "DualEndpointRW" class

Dual endpoint model with RW prior for biomarker

Initialization function for '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

Initialization function for '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

Initialization function for "DualSimulations"

Class for the simulations output from dual-endpoint model based designs

Class for the summary of dual-endpoint simulations output

Initialization function for the "EffFlexi" class

Class for the efficacy model in flexible form for prior expressed in form of pseudo data

Initialization function for the "Effloglog" class

Class for the linear log-log efficacy model using pseudo data prior

Compute the expected efficacy based on a given dose, a given pseudo Efficacy log-log model and a given efficacy sample

Class for general data input

No Intitialization function for this General class for model input

Initialization function for "GeneralSimulations"

General class for the simulations output

Class for the summary of general simulations output

Initialization function for "IncrementMin"

Max increment based on minimum of multiple increment rules

The virtual class for controlling increments

Initialization function for "IncrementsNumDoseLevels"

Increments control based on number of dose levels

Initialization function for "IncrementsRelative"

Increments control based on relative differences in intervals

Initialization function for "IncrementsRelativeDLT"

Increments control based on relative differences in terms of DLTs

Initialization function for "IncrementsRelativeParts"

Increments control based on relative differences in intervals, with special rules for part 1 and beginning of part 2

Intialization function for "LogisticIndepBeta" class

No initialization function Standard logistic model with prior in form of pseudo data

Initialization function for the "LogisticKadane" class

Reparametrized logistic model

Initialization function for the "LogisticLogNormal" class

Standard logistic model with bivariate (log) normal prior

Initialization function for the "LogisticLogNormalMixture" class

Standard logistic model with online mixture of two bivariate log normal priors

Initialization function for the "LogisticLogNormalSub" class

Standard logistic model with bivariate (log) normal prior with substractive dose standardization

Initialization function for the "LogisticNormal" class

Standard logistic model with bivariate normal prior

Initialization function for the "LogisticNormalFixedMixture" class

Standard logistic model with fixed mixture of multiple bivariate (log) normal priors

Initialization function for the "LogisticNormalMixture" class

Standard logistic model with flexible mixture of two bivariate normal priors

Initialization function for the "McmcOptions" class

Class for the three canonical MCMC options

Construct a minimally informative prior

Class for the model input

No Initialization function class for Efficacy models using pseudo data prior

Class of models using expressing their prior in form of Pseudo data

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

The virtual class for finding next best dose

Initialization function for "NextBestDualEndpoint"

The class with the input for finding the next dose based on the dual endpoint model

Initialization function for class "NextBestMTD"

The class with the input for finding the next best MTD estimate

Initialization function for the class 'NextBestMaxGain'

Next best dose with maximum gain value based on a pseudo DLE and efficacy model without samples

Initialization function for class "NextBestMaxGainSamples"

Next best dose with maximum gain value based on a pseudo DLE and efficacy model with samples

Initialization function for "NextBestNCRM"

The class with the input for finding the next dose in target interval

Initialization function for the class "NextBestTD"

Next best dose based on Pseudo DLE model without sample

Initialization function for class "NextBestTDsamples"

Next best dose based on Pseudo DLE Model with samples

Initialization function for "NextBestThreePlusThree"

The class with the input for finding the next dose in target interval

Initialization function for the "ProbitLogNormal" class

Probit model with bivariate log normal prior

Initialization function for '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.

Initialization function for 'DualPseudoSimulations' 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.

Class for the summary of the dual responses simulations using pseudo models

Initialization function of the '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.

Class for the summary of pseudo-models simulations output

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

A Reference Class to represent sequentially updated reporting objects.

Initialization function for "RuleDesign"

Class for rule-based designs

Initialization function for "Samples"

Class for the MCMC output

Initialization function for the "Simulations" class

Class for the simulations output from model based designs

Class for the summary of model-based simulations output

The virtual class for stopping rules

Initialization function for "StoppingAll"

Stop based on fullfillment of all multiple stopping rules

Initialization function for "StoppingAny"

Stop based on fullfillment of any stopping rule

Initialization function for "StoppingCohortsNearDose"

Stop based on number of cohorts near to next best dose

Initialization function for "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.

Initialization function for "StoppingHighestDose"

Stop when the highest dose is reached

Initialization function for "StoppingList"

Stop based on multiple stopping rules

Initialization function for "StoppingMTDdistribution"

Stop based on MTD distribution

Initialization function for "StoppingMinCohorts"

Stop based on minimum number of cohorts

Initialization function for "StoppingMinPatients"

Stop based on minimum number of patients

Initialization function for "StoppingPatientsNearDose"

Stop based on number of patients near to next best dose

Initialization function for "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

Initialization function for "StoppingTargetBiomarker"

Stop based on probability of target biomarker

Initialization function for "StoppingTargetProb"

Stop based on probability of target tox interval

Initialization function for 'TDDesign' class

Design class using DLE responses only based on the pseudo DLE model without sample

Initialization function for '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:

Creates a new 3+3 design object from a dose grid

A Reference Class to help programming validation for new S4 classes

The method combining two atomic stopping rules

The method combining an atomic and a stopping list

The method combining a stopping list and an atomic

Approximate posterior with (log) normal distribution

as.list method for the "GeneralData" class

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

Object-oriented implementation of CRM designs

Open the example pdf for crmPack

Open the browser with help pages for crmPack

Compute the density of Inverse gamma distribution

Compute the doses for a given probability, given model and samples

Obtain hypothetical trial course table for a design

Fit method for the Samples class

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

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

Get specific parameter samples and produce a data.frame

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)

Get the minimal informative unimodal beta distribution

Helper function to obtain simulation results list

Initialization method for the "DualEndpointOld" class

Predicate checking for a boolean option

Predicate checking for a probability range

Predicate checking for a probability

Predicate checking for a numeric range

Checking for scalar

checks for whole numbers (integers)

Helper function to join two function bodies

Helper function to join two BUGS models

Shorthand for logit function

Helper function for value matching with tolerance

Determine the maximum possible next dose

"MAX" combination of cohort size rules

Obtain posterior samples for all model parameters

"MIN" combination of cohort size rules

Multiple plot function

Convenience function to make barplots of percentages

Do MCMC sampling for Bayesian logistic regression model

Find the next best dose

Check overlap of two character vectors

The method combining two atomic stopping rules

The method combining a stopping list and an atomic

The method combining an atomic and a stopping list

Compute the distribution function of Inverse gamma distribution

Plot of the fitted dose-tox based with a given pseudo DLE model and data without samples

Plot method for the "Data" class

Plot of the fitted dose-efficacy based with a given pseudo efficacy model and data without samples

Plot method for the "DataDual" class

Plot dual-endpoint simulations

Plot summaries of the dual-endpoint design simulations

Plot simulations

Graphical display of the general simulation summary

This plot method can be applied to PseudoDualFlexiSimulations objects in order to summarize them graphically. Possible types of plots at the moment are: trajectorySummary of the trajectory of the simulated trials dosesTriedAverage proportions of the doses tested in patients sigma2The variance of the efficacy responses sigma2betaWThe variance of the random walk model

Plot simulations

Plot the summary of Pseudo Dual Simulations summary

Plot summaries of the pseudo simulations

Plotting dose-toxicity and dose-biomarker model fits

Plotting dose-toxicity model fits

Plot the fitted dose-effcacy curve using a model from ModelEff class with samples

Plot the fitted dose-DLE curve using a ModelTox class model with samples

Plot summaries of the model-based design simulations

Plots gtable objects

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

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

Taken from utils package (print.vignette)

Compute the probability for a given dose, given model and samples

Shorthand for probit function

Compute the quantile function of Inverse gamma distribution

The random generation of the Inverse gamma distribution

Safe conversion to integer vector

Compute the number of samples for a given MCMC options triple

Determine if we should save this sample

Helper function to set and save the RNG seed

Show the summary of the dual-endpoint simulations

Show the summary of the simulations

Show the summary of Pseudo Dual simulations summary

Show the summary of the simulations

Show the summary of the simulations

Simulate outcomes from a CRM design

Simulate outcomes from a dual-endpoint design

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

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 outcomes from a rule-based design

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.

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

Determine the size of the next cohort

Stop the trial?

Summarize the dual-endpoint design simulations, relative to given true dose-toxicity and dose-biomarker curves

Summarize the simulations, relative to a given truth

Summary for Pseudo Dual responses simulations given a pseudo DLE model and the Flexible efficacy model.

Summary for Pseudo Dual responses simulations, relative to a given pseudo DLE and efficacy model (except the EffFlexi class model)

Summarize the simulations, relative to a given truth

Summarize the model-based design simulations, relative to a given truth

Update method for the "Data" class

Update method for the "DataDual" class

Update method for the "DataParts" class

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 for the 'Effloglog' Model class. This is a method to update the modal estimates of the model parameters θ_1 (theta1), θ_2 (theta2) and ν (nu, the precision of the efficacy responses) when new data or new observations of responses are available and added in.

Update method for the 'LogisticIndepBeta'Model class. This is a method to update the modal estimates of the model parameters φ_1 (phi1) and φ_2 (phi2) when new data or new observations of responses are available and added in.

Creating a WinBUGS model file