Package index • crmPack

h_model_dual_endpoint_sigma2betaW() stable: Update certain components of DualEndpoint model with regard to prior variance factor of the random walk.

h_model_dual_endpoint_beta() stable: Update certain components of DualEndpoint model with regard to parameters of the function that models dose-biomarker relationship defined in the DualEndpointBeta class.

h_info_theory_dist() experimental: Calculating the Information Theoretic Distance

h_next_best_mg_ci() experimental: Credibility Intervals for Max Gain and Target Doses at nextBest-NextBestMaxGain Method.

h_next_best_mg_doses_at_grid() experimental: Get Closest Grid Doses for a Given Target Doses for nextBest-NextBestMaxGain Method.

h_next_best_eligible_doses() experimental: Get Eligible Doses from the Dose Grid.

h_next_best_ncrm_loss_plot() experimental: Building the Plot for nextBest-NextBestNCRMLoss Method.

h_next_best_tdsamples_plot() experimental: Building the Plot for nextBest-NextBestTDsamples Method.

h_next_best_td_plot() experimental: Building the Plot for nextBest-NextBestTD Method.

h_next_best_mg_plot() experimental: Building the Plot for nextBest-NextBestMaxGain Method.

h_next_best_mgsamples_plot() experimental: Building the Plot for nextBest-NextBestMaxGainSamples Method.

h_obtain_dose_grid_range(): Helper Function Containing Common Functionality

h_covr_active() h_covr_detrace() h_is_covr_trace() h_covr_detrace_call(): Helpers for stripping expressions of covr-inserted trace code

h_default_if_empty() stable: Getting the default value for an empty object

h_unpack_stopit(): Helper function to recursively unpack stopping rules and return lists with logical value and label given

h_calc_report_label_percentage(): Helper function to calculate percentage of true stopping rules for report label output calculates true column means and converts output into percentages before combining the output with the report label; output is passed to show() and output with cat to console

h_validate_common_data_slots(): Helper Function performing validation Common to Data and DataOrdinal

h_summarize_add_stats(): Helper function to calculate average across iterations for each additional reporting parameter extracts parameter names as specified by user and averaged the values for each specified parameter to show() and output with cat to console

h_determine_dlts(): Helper function to determine the dlts including first separate and placebo condition

Internal Validation Functions

v_general_data() h_doses_unique_per_cohort() v_data() v_data_dual() v_data_parts() v_data_mixture() v_data_da() v_data_ordinal() v_data_grouped() stable: Internal Helper Functions for Validation of GeneralData Objects

v_mcmc_options() stable: Internal Helper Functions for Validation of McmcOptions Objects

v_model_params_normal() experimental: Internal Helper Functions for Validation of Model Parameters Objects

v_general_model() v_model_logistic_kadane() v_model_logistic_kadane_beta_gamma() v_model_logistic_normal_mix() v_model_logistic_normal_fixed_mix() v_model_logistic_log_normal_mix() v_model_dual_endpoint() v_model_dual_endpoint_rw() v_model_dual_endpoint_beta() v_model_dual_endpoint_emax() v_model_logistic_indep_beta() v_model_eff_log_log() v_model_eff_flexi() v_model_da_logistic_log_normal() v_model_tite_logistic_log_normal() v_model_one_par_exp_normal_prior() v_model_one_par_exp_prior() v_logisticlognormalordinal() stable: Internal Helper Functions for Validation of GeneralModel and ModelPseudo Objects

v_samples(): Internal Helper Functions for Validation of Samples Objects

v_next_best_mtd() v_next_best_ncrm() v_next_best_ncrm_loss() v_next_best_dual_endpoint() v_next_best_min_dist() v_next_best_inf_theory() v_next_best_td() v_next_best_td_samples() v_next_best_max_gain_samples() v_next_best_prob_mtd_lte() v_next_best_prob_mtd_min_dist() v_next_best_ordinal() stable: Internal Helper Functions for Validation of NextBest Objects

v_increments_relative() v_increments_relative_parts() v_increments_relative_dlt() v_increments_dose_levels() v_increments_hsr_beta() v_increments_min() v_increments_maxtoxprob() v_increments_ordinal() v_cohort_size_ordinal() stable: Internal Helper Functions for Validation of Increments Objects

v_starting_dose() experimental: Internal Helper Functions for Validation of StartingDose Objects

v_stopping_cohorts_near_dose() v_stopping_patients_near_dose() v_stopping_min_cohorts() v_stopping_min_patients() v_stopping_target_prob() v_stopping_mtd_distribution() v_stopping_mtd_cv() v_stopping_target_biomarker() v_stopping_list() v_stopping_all() v_stopping_tdci_ratio() stable: Internal Helper Functions for Validation of Stopping Objects

v_cohort_size_range() v_cohort_size_dlt() v_cohort_size_const() v_cohort_size_parts() v_cohort_size_max() stable: Internal Helper Functions for Validation of CohortSize Objects

v_safety_window_size() v_safety_window_const() stable: Internal Helper Functions for Validation of SafetyWindow Objects

v_rule_design() v_rule_design_ordinal() v_design_grouped() stable: Internal Helper Functions for Validation of RuleDesign Objects

v_general_simulations() v_simulations() v_dual_simulations() v_da_simulations() stable: Internal Helper Functions for Validation of GeneralSimulations Objects

v_pseudo_simulations() v_pseudo_dual_simulations() v_pseudo_dual_flex_simulations() stable: Internal Helper Functions for Validation of PseudoSimulations Objects

Custom Checkmate Assertions

check_probabilities() assert_probabilities() test_probabilities() expect_probabilities() stable: Check if an argument is a probability vector

check_probability() assert_probability() test_probability() expect_probability() stable: Check if an argument is a single probability value

check_probability_range() assert_probability_range() test_probability_range() expect_probability_range() stable: Check if an argument is a probability range

check_length() assert_length() test_length() stable: Check if vectors are of compatible lengths

check_range() assert_range() test_range() expect_range() stable: Check that an argument is a numerical range

Methods

h_plot_data_dataordinal() plot(<Data>,<missing>) plot(<DataOrdinal>,<missing>) stable: Helper Function for the Plot Method of the Data and DataOrdinal Classes

plot(<DataDual>,<missing>) stable: Plot Method for the DataDual Class

plot(<DataDA>,<missing>) stable: Plot Method for the DataDA Class

update(<Data>) stable: Updating Data Objects

update(<DataParts>) stable: Updating DataParts Objects

update(<DataDual>) stable: Updating DataDual Objects

update(<DataDA>) stable: Updating DataDA Objects

update(<DataOrdinal>) experimental: Updating DataOrdinal Objects

getEff() stable: Extracting Efficacy Responses for Subjects Categorized by the DLT

ngrid() stable: Number of Doses in Grid

dose_grid_range() stable: Getting the Dose Grid Range

saveSample() stable: Determining if this Sample Should be Saved

size() stable: Size of an Object

doseFunction() experimental: Getting the Dose Function for a Given Model Type

dose() stable: Computing the Doses for a given independent variable, Model and Samples

probFunction() experimental: Getting the Prob Function for a Given Model Type

prob() stable: Computing Toxicity Probabilities for a Given Dose, Model and Samples

efficacyFunction() experimental: Getting the Efficacy Function for a Given Model Type

efficacy() stable: Computing Expected Efficacy for a Given Dose, Model and Samples

biomarker() experimental: Get the Biomarker Levels for a Given Dual-Endpoint Model, Given Dose Levels and Samples

gain() stable: Compute Gain Values based on Pseudo DLE and a Pseudo Efficacy Models and Using Optional Samples.

update(<ModelPseudo>): Update method for the ModelPseudo model class. This is a method to update the model class slots (estimates, parameters, variables and etc.), when the new data (e.g. new observations of responses) are available. This method is mostly used to obtain new modal estimates for pseudo model parameters.

mcmc() stable: Obtaining Posterior Samples for all Model Parameters

names(<Samples>) stable: The Names of the Sampled Parameters

nextBest() stable: Finding the Next Best Dose

stopTrial() stable: Stop the trial?

maxDose() stable: Determine the Maximum Possible Next Dose

Functions

enable_logging() disable_logging() is_logging_enabled() log_trace() experimental: Verbose Logging

dapply() experimental: Apply a Function to Subsets of Data Frame.

knit_print(<CohortSizeConst>) knit_print(<CohortSizeRange>) knit_print(<CohortSizeDLT>) knit_print(<CohortSizeParts>) knit_print(<CohortSizeMax>) knit_print(<CohortSizeMin>) knit_print(<CohortSizeOrdinal>) knit_print(<StartingDose>) knit_print(<RuleDesign>) knit_print(<Design>) knit_print(<DualDesign>) knit_print(<DADesign>) knit_print(<TDDesign>) knit_print(<DualResponsesDesign>) knit_print(<DesignOrdinal>) knit_print(<DesignGrouped>) knit_print(<TDsamplesDesign>) knit_print(<DualResponsesSamplesDesign>) knit_print(<RuleDesignOrdinal>) knit_print(<GeneralData>) knit_print(<DataParts>) knit_print(<DualEndpoint>) knit_print(<ModelParamsNormal>) knit_print(<GeneralModel>) knit_print(<LogisticKadane>) knit_print(<LogisticKadaneBetaGamma>) knit_print(<LogisticLogNormal>) knit_print(<LogisticLogNormalMixture>) knit_print(<LogisticLogNormalSub>) knit_print(<LogisticNormalMixture>) knit_print(<LogisticNormalFixedMixture>) knit_print(<OneParLogNormalPrior>) knit_print(<OneParExpPrior>) knit_print(<LogisticLogNormalGrouped>) knit_print(<LogisticLogNormalOrdinal>) knit_print(<LogisticIndepBeta>) knit_print(<Effloglog>) knit_print(<IncrementsRelative>) knit_print(<IncrementsRelativeDLT>) knit_print(<IncrementsDoseLevels>) knit_print(<IncrementsHSRBeta>) knit_print(<IncrementsMin>) knit_print(<IncrementsOrdinal>) knit_print(<IncrementsRelativeParts>) knit_print(<IncrementsRelativeDLTCurrent>) knit_print(<NextBestMTD>) knit_print(<NextBestNCRM>) knit_print(<NextBestThreePlusThree>) knit_print(<NextBestDualEndpoint>) knit_print(<NextBestMinDist>) knit_print(<NextBestInfTheory>) knit_print(<NextBestTD>) knit_print(<NextBestMaxGain>) knit_print(<NextBestProbMTDLTE>) knit_print(<NextBestProbMTDMinDist>) knit_print(<NextBestNCRMLoss>) knit_print(<NextBestTDsamples>) knit_print(<NextBestMaxGainSamples>) knit_print(<NextBestOrdinal>) knit_print(<SafetyWindow>) knit_print(<SafetyWindowConst>) knit_print(<SafetyWindowSize>) knit_print(<StoppingOrdinal>) knit_print(<StoppingMaxGainCIRatio>) knit_print(<StoppingList>) knit_print(<StoppingAny>) knit_print(<StoppingAll>) knit_print(<StoppingTDCIRatio>) knit_print(<StoppingTargetBiomarker>) knit_print(<StoppingLowestDoseHSRBeta>) knit_print(<StoppingMTDCV>) knit_print(<StoppingMTDdistribution>) knit_print(<StoppingHighestDose>) knit_print(<StoppingSpecificDose>) knit_print(<StoppingTargetProb>) knit_print(<StoppingMinCohorts>) knit_print(<StoppingMinPatients>) knit_print(<StoppingPatientsNearDose>) knit_print(<StoppingCohortsNearDose>) knit_print(<StoppingMissingDose>) experimental: Render a CohortSizeConst Object

Classes

CohortSizeOrdinal() .DefaultCohortSizeOrdinal() experimental: CohortSizeOrdinal

IncrementsOrdinal() .DefaultIncrementsOrdinal() experimental: IncrementsOrdinal

DADesign() .DefaultDADesign() stable: DADesign

.DefaultDASimulations(): Class for the simulations output from DA based designs

DASimulations(): Initialization function for DASimulations

DualResponsesDesign() .DefaultDualResponsesDesign() stable: DualResponsesDesign.R

DualResponsesSamplesDesign() .DefaultDualResponsesSamplesDesign() stable: DualResponsesSamplesDesign

DualSimulations() .DefaultDualSimulations() stable: DualSimulations

.DefaultDualSimulationsSummary() stable: DualSimulationsSummary

GeneralSimulations() .DefaultGeneralSimulations() stable: GeneralSimulations @description This class captures trial simulations. Here also the random generator state before starting the simulation is saved, in order to be able to reproduce the outcome. For this just use set.seed with the seed as argument before running simulate,Design-method.

.DefaultGeneralSimulationsSummary() .DefaultPseudoSimulationsSummary() stable: GeneralSimulationsSummary

IncrementsMaxToxProb() .DefaultIncrementsMaxToxProb() experimental: IncrementsMaxToxProb

LogisticLogNormalOrdinal() .DefaultLogisticLogNormalOrdinal() experimental: LogisticLogNormalOrdinal

MinimalInformative(): Construct a minimally informative prior

NextBestOrdinal() .DefaultNextBestOrdinal() experimental: NextBestOrdinal

.DefaultPseudoDualFlexiSimulations(): 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.

PseudoDualFlexiSimulations(): Initialization function for 'PseudoDualFlexiSimulations' class

PseudoDualSimulations() .DefaultPseudoDualSimulations() stable: PseudoDualSimulations

.DefaultPseudoDualSimulationsSummary(): Class for the summary of the dual responses simulations using pseudo models

PseudoSimulations() .DefaultPseudoSimulations() stable: PseudoSimulations

PseudoSimulationsSummary-class .PseudoSimulationsSummary: Class for the summary of pseudo-models simulations output

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

Report: A Reference Class to represent sequentially updated reporting objects.

Simulations() .DefaultSimulations() stable: Simulations

.DefaultSimulationsSummary() stable: SimulationsSummary

StoppingOrdinal() .DefaultStoppingOrdinal() experimental: StoppingOrdinal

approximate(): Approximate posterior with (log) normal distribution

assertions experimental: Additional Assertions for checkmate

check_equal() assert_equal() experimental: Check if All Arguments Are Equal

check_format() assert_format() test_format() expect_format() stable: Check that an argument is a valid format specification

crmPackExample(): Open the example pdf for crmPack

crmPackHelp(): Open the browser with help pages for crmPack

examine(): Obtain hypothetical trial course table for a design

fit(): Fit method for the Samples class

fitGain(): Get the fitted 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

fitPEM(): Get the fitted DLT free survival (piecewise exponential model). This function returns a data frame with dose, middle, lower and upper quantiles for the PEM curve. If hazard=TRUE,

get(<Samples>,<character>): Get specific parameter samples and produce a data.frame

h_get_min_inf_beta(): Helper for Minimal Informative Unimodal Beta Distribution

logit(): Shorthand for logit function

match_within_tolerance(): Helper function for value matching with tolerance

maxSize(): "MAX" combination of cohort size rules

minSize(): "MIN" combination of cohort size rules

`|`(<Stopping>,<Stopping>): The method combining two atomic stopping rules

`|`(<StoppingAny>,<Stopping>): The method combining a stopping list and an atomic

`|`(<Stopping>,<StoppingAny>): The method combining an atomic and a stopping list

plot(<Data>,<ModelTox>): Plot of the fitted dose-tox based with a given pseudo DLE model and data without samples

plot(<DataDual>,<ModelEff>): Plot of the fitted dose-efficacy based with a given pseudo efficacy model and data without samples

plot(<DualSimulations>,<missing>): Plot dual-endpoint simulations

plot(<DualSimulationsSummary>,<missing>): Plot summaries of the dual-endpoint design simulations

plot(<GeneralSimulations>,<missing>): Plot simulations

plot(<GeneralSimulationsSummary>,<missing>): Graphical display of the general simulation summary

plot(<PseudoDualFlexiSimulations>,<missing>)

This plot method can be applied to PseudoDualFlexiSimulations objects in order to summarize them graphically. Possible types of plots at the moment are:

trajectory: Summary of the trajectory of the simulated trials
dosesTried: Average proportions of the doses tested in patients
sigma2: The variance of the efficacy responses
sigma2betaW: The variance of the random walk model

You can specify one or both of these in the type argument.

plot(<PseudoDualSimulations>,<missing>): Plot simulations

plot(<PseudoDualSimulationsSummary>,<missing>): Plot the summary of Pseudo Dual Simulations summary

plot(<PseudoSimulationsSummary>,<missing>): Plot summaries of the pseudo simulations

plot(<Samples>,<DALogisticLogNormal>): Plotting dose-toxicity model fits

plot(<Samples>,<DualEndpoint>): Plotting dose-toxicity and dose-biomarker model fits

plot(<Samples>,<GeneralModel>): Plotting dose-toxicity model fits

plot(<Samples>,<ModelEff>): Plot the fitted dose-efficacy curve using a model from ModelEff class with samples

plot(<Samples>,<ModelTox>): Plot the fitted dose-DLE curve using a ModelTox class model with samples

plot(<SimulationsSummary>,<missing>): Plot summaries of the model-based design simulations

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

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

plot(<gtable>) print(<gtable>): Plot gtable Objects

probit(): Shorthand for probit function

set_seed() stable: Helper Function to Set and Save the RNG Seed

show(<DualSimulationsSummary>): Show the summary of the dual-endpoint simulations

show(<GeneralSimulationsSummary>): Show the summary of the simulations

show(<PseudoDualSimulationsSummary>): Show the summary of Pseudo Dual simulations summary

show(<PseudoSimulationsSummary>): Show the summary of the simulations

show(<SimulationsSummary>): Show the summary of the simulations

simulate(<DADesign>): Simulate outcomes from a time-to-DLT augmented CRM design (DADesign)

simulate(<Design>): Simulate outcomes from a CRM design

simulate(<DualDesign>): Simulate outcomes from a dual-endpoint design

simulate(<DualResponsesDesign>): 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(<DualResponsesSamplesDesign>): 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(<RuleDesign>): Simulate outcomes from a rule-based design

simulate(<TDDesign>): 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(<TDsamplesDesign>): 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

simulate(<DesignGrouped>) experimental: Simulate Method for the DesignGrouped Class

summary(<DualSimulations>): Summarize the dual-endpoint design simulations, relative to given true dose-toxicity and dose-biomarker curves

summary(<GeneralSimulations>): Summarize the simulations, relative to a given truth

summary(<PseudoDualFlexiSimulations>): Summary for Pseudo Dual responses simulations given a pseudo DLE model and the Flexible efficacy model.

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

summary(<Simulations>): Summarize the model-based design simulations, relative to a given truth

summary(<PseudoSimulations>): Summarize the simulations, relative to a given truth

tidy() experimental: Tidying CrmPackClass objects

windowLength(): Determine the safety window length of the next cohort

`&`(<Stopping>,<Stopping>): The method combining two atomic stopping rules

`&`(<Stopping>,<StoppingAll>): The method combining an atomic and a stopping list

`&`(<StoppingAll>,<Stopping>): The method combining a stopping list and an atomic