Package index • escalation

All functions

CorrelatedPatientSample: A sample of patients that experience correlated events in simulations.

PatientSample: A sample of patients to use in simulations.

as_tibble(<dose_paths>): Cast dose_paths object to tibble.

as_tibble(<selector>): Cast dose_selector object to tibble.

as_tibble(<simulations_collection>): Convert a simulations_collection to a tibble

boin12_rds(): Tabulate rank-based desirability scores for a BOIN12 trial

calculate_probabilities(): Calculate dose-path probabilities

check_dose_selector_consistency(): Check the consistency of a dose_selector instance

check_simulations_consistency(): Check the consistency of a dose_selector instance

cohort(): Cohort numbers of evaluated patients.

cohorts_of_n(): Sample times between patient arrivals using the exponential distribution.

combo_selector(): Dose selector for combinations of treatments

continue(): Should this dose-finding experiment continue?

convergence_plot(): Plot the convergence processes from a collection of simulations.

crystallised_dose_paths(): Dose-paths with probabilities attached.

demand_n_at_dose(): Demand there are n patients at a dose before condisdering stopping.

dont_skip_doses(): Prevent skipping of doses.

dose_admissible(): Is each dose admissible?

dose_escalation_table(): Plot a table of dose escalation vs de-escalation vs stop decisions

dose_indices(): Dose indices

dose_paths(): Dose pathways

dose_paths_function(): Get function for calculating dose pathways.

dose_string_to_vector(): Go from a single multi-treatment dose string to a vector of dose-indices

dose_strings(): Dose strings

dose_vector_to_string(): Go from a single multi-treatment vector of dose-indices to a dose string

doses_given(): Doses given to patients.

eff(): Binary efficacy outcomes.

eff_at_dose(): Number of toxicities seen at each dose.

eff_limit(): Efficacy rate limit

empiric_eff_rate(): Observed efficacy rate at each dose.

empiric_tox_rate(): Observed toxicity rate at each dose.

enforce_three_plus_three(): Enforce that a trial path has followed the 3+3 method.

expand_last_dose_to_cohort(): Expand the cohort of the last given dose to at least n patients

fit(): Fit a dose-finding model.

follow_path(): Follow a pre-determined dose administration path.

get_boin(): Get an object to fit the BOIN model using the BOIN package.

get_boin12(): Get an object to fit the BOIN12 model for phase I/II dose-finding.

get_boin_comb(): Get an object to fit the BOIN COMB model using the BOIN package.

get_dfcrm(): Get an object to fit the CRM model using the dfcrm package.

get_dfcrm_tite(): Get an object to fit the TITE-CRM model using the dfcrm package.

get_dose_combo_indices(): Get all combinations of dose indices

get_dose_paths(): Calculate future dose paths.

get_empiric_crm_skeleton_weights(): Get posterior model weights for several empiric CRM skeletons.

get_mtpi(): Get an object to fit the mTPI dose-finding model.

get_mtpi2(): Get an object to fit the mTPI-2 dose-finding model.

get_potential_outcomes(): Get potential outcomes from a list of PatientSamples

get_random_selector(): Get an object to fit a dose-selector that randomly selects doses.

get_three_plus_three(): Get an object to fit the 3+3 model.

get_tpi(): Get an object to fit the TPI dose-finding model.

get_trialr_crm(): Get an object to fit the CRM model using the trialr package.

get_trialr_crm_tite(): Get an object to fit the TITE-CRM model using the trialr package.

get_trialr_efftox(): Get an object to fit the EffTox model using the trialr package.

get_trialr_nbg(): Get an object to fit the NBG dose-finding model using the trialr package.

get_trialr_nbg_tite(): Get an object to fit a TITE version of the NBG dose-finding model using trialr

get_wages_and_tait(): Get an object to fit Wages & Tait's model for phase I/II dose-finding.

graph_paths(): Visualise dose-paths as a graph

is_randomising(): Is this selector currently randomly allocating doses?

linear_follow_up_weight(): Weights for tolerance and toxicity events using linear function of time

mean_prob_eff(): Mean efficacy rate at each dose.

mean_prob_tox(): Mean toxicity rate at each dose.

median_prob_eff(): Median efficacy rate at each dose.

median_prob_tox(): Median toxicity rate at each dose.

model_frame(): Model data-frame.

n_at_dose(): Number of patients treated at each dose.

n_at_recommended_dose(): Number of patients treated at the recommended dose.

num_cohort_outcomes(): Number of different possible outcomes for a cohort of patients

num_dose_path_nodes(): Number of nodes in dose-paths analysis

num_doses(): Number of doses.

num_eff(): Total number of efficacies seen.

num_patients(): Number of patients evaluated.

num_tox(): Total number of toxicities seen.

parse_phase1_2_outcomes(): Parse a string of phase I/II dose-finding outcomes to vector notation.

parse_phase1_outcomes(): Parse a string of phase I dose-finding outcomes to vector notation.

phase1_2_outcomes_to_cohorts(): Break a phase I/II outcome string into a list of cohort parts.

phase1_outcomes_to_cohorts(): Break a phase I outcome string into a list of cohort parts.

prob_administer(): Percentage of patients treated at each dose.

prob_eff_quantile(): Quantile of the efficacy rate at each dose.

prob_recommend(): Probability of recommendation

prob_tox_exceeds() prob_eff_exceeds(): Probability that the toxicity rate exceeds some threshold.

prob_tox_quantile(): Quantile of the toxicity rate at each dose.

prob_tox_samples() prob_eff_samples(): Get samples of the probability of toxicity.

recommended_dose(): Recommended dose for next patient or cohort.

select_boin12_obd(): Select dose by BOIN12's OBD-choosing algorithm.

select_boin_comb_mtd(): Select dose by BOIN-COMB's MTD-choosing algorithm.

select_boin_mtd(): Select dose by BOIN's MTD-choosing algorithm.

select_dose_by_cibp(): Select dose by the CIBP selection criterion.

select_mtpi2_mtd(): Select dose by mTPI2's MTD-choosing algorithm.

select_mtpi_mtd(): Select dose by mTPI's MTD-choosing algorithm.

select_tpi_mtd(): Select dose by TPI's MTD-choosing algorithm.

selector(): Dose selector.

selector_factory(): Dose selector factory.

simulate_compare(): Simulate clinical trials for several designs using common patients.

simulate_trials(): Simulate clinical trials.

simulation_function(): Get function for simulating trials.

simulations(): Simulated trials.

simulations_collection(): Make an instance of type simulations_collection

spread_paths(): Spread the information in dose_finding_paths object to a wide data.frame format.

stack_sims_vert(): Stack simulations_collection results vertically

stick_on_num_tox(): Stay at the current dose when num_tox of num_patients have experienced tox

stop_at_n(): Stop when there are n patients in total.

stop_when_n_at_dose(): Stop when there are n patients at a dose.

stop_when_too_toxic(): Stop trial and recommend no dose when a dose is too toxic.

stop_when_tox_ci_covered(): Stop when uncertainty interval of prob tox is covered.

supports_sampling(): Does this selector support sampling of outcomes?

three_plus_three(): Fit the 3+3 model to some outcomes.

tox(): Binary toxicity outcomes.

tox_at_dose(): Number of toxicities seen at each dose.

tox_limit(): Toxicity rate limit

tox_target(): Target toxicity rate

trial_duration(): Duration of trials.

try_rescue_dose(): Demand that a rescue dose is tried before stopping is permitted.

unadmit_untested(): Make untested and unrecommended doses inadmissible.

utility(): Utility score of each dose.

weight(): Outcome weights.