A Modular Approach to Dose-Finding Clinical Trials
Cast dose_paths object to tibble.
Cast dose_selector object to tibble.
Convert a simulations_collection to a tibble
Tabulate rank-based desirability scores for a BOIN12 trial
Calculate dose-path probabilities
Check the consistency of a dose_selector instance
Check the consistency of a dose_selector instance
Cohort numbers of evaluated patients.
Sample times between patient arrivals using the exponential distributi...
Dose selector for combinations of treatments
Should this dose-finding experiment continue?
Plot the convergence processes from a collection of simulations.
A sample of patients that experience correlated events in simulations.
Dose-paths with probabilities attached.
Demand there are n patients at a dose before condisdering stopping.
Prevent skipping of doses.
Is each dose admissible?
Plot a table of dose escalation vs de-escalation vs stop decisions
Dose indices
Get function for calculating dose pathways.
Dose pathways
Go from a single multi-treatment dose string to a vector of dose-indic...
Dose strings
Go from a single multi-treatment vector of dose-indices to a dose stri...
Doses given to patients.
Number of toxicities seen at each dose.
Efficacy rate limit
Binary efficacy outcomes.
Observed efficacy rate at each dose.
Observed toxicity rate at each dose.
Enforce that a trial path has followed the 3+3 method.
The 'escalation' package.
Expand the cohort of the last given dose to at least n patients
Fit a dose-finding model.
Follow a pre-determined dose administration path.
Get an object to fit the BOIN COMB model using the BOIN package.
Get an object to fit the BOIN model using the BOIN package.
Get an object to fit the BOIN12 model for phase I/II dose-finding.
Get an object to fit the TITE-CRM model using the dfcrm package.
Get an object to fit the CRM model using the dfcrm package.
Get all combinations of dose indices
Calculate future dose paths.
Get posterior model weights for several empiric CRM skeletons.
Get an object to fit the mTPI dose-finding model.
Get an object to fit the mTPI-2 dose-finding model.
Get potential outcomes from a list of PatientSamples
Get an object to fit a dose-selector that randomly selects doses.
Get an object to fit the 3+3 model.
Get an object to fit the TPI dose-finding model.
Get an object to fit the TITE-CRM model using the trialr package.
Get an object to fit the CRM model using the trialr package.
Get an object to fit the EffTox model using the trialr package.
Get an object to fit a TITE version of the NBG dose-finding model usin...
Get an object to fit the NBG dose-finding model using the trialr packa...
Get an object to fit Wages & Tait's model for phase I/II dose-finding.
Visualise dose-paths as a graph
Is this selector currently randomly allocating doses?
Weights for tolerance and toxicity events using linear function of tim...
Mean efficacy rate at each dose.
Mean toxicity rate at each dose.
Median efficacy rate at each dose.
Median toxicity rate at each dose.
Model data-frame.
Number of patients treated at each dose.
Number of patients treated at the recommended dose.
Number of different possible outcomes for a cohort of patients
Number of nodes in dose-paths analysis
Number of doses.
Total number of efficacies seen.
Number of patients evaluated.
Total number of toxicities seen.
Parse a string of phase I/II dose-finding outcomes to vector notation.
Parse a string of phase I dose-finding outcomes to vector notation.
A sample of patients to use in simulations.
Break a phase I/II outcome string into a list of cohort parts.
Break a phase I outcome string into a list of cohort parts.
Percentage of patients treated at each dose.
Quantile of the efficacy rate at each dose.
Probability of recommendation
Probability that the toxicity rate exceeds some threshold.
Quantile of the toxicity rate at each dose.
Get samples of the probability of toxicity.
Recommended dose for next patient or cohort.
Select dose by BOIN-COMB's MTD-choosing algorithm.
Select dose by BOIN's MTD-choosing algorithm.
Select dose by BOIN12's OBD-choosing algorithm.
Select dose by the CIBP selection criterion.
Select dose by mTPI's MTD-choosing algorithm.
Select dose by mTPI2's MTD-choosing algorithm.
Select dose by TPI's MTD-choosing algorithm.
Dose selector factory.
Dose selector.
Simulate clinical trials for several designs using common patients.
Simulate clinical trials.
Get function for simulating trials.
Make an instance of type simulations_collection
Simulated trials.
Spread the information in dose_finding_paths object to a wide data.fra...
Stack simulations_collection results vertically
Stay at the current dose when num_tox of num_patients have experienced...
Stop when there are n patients in total.
Stop when there are n patients at a dose.
Stop trial and recommend no dose when a dose is too toxic.
Stop when uncertainty interval of prob tox is covered.
Does this selector support sampling of outcomes?
Fit the 3+3 model to some outcomes.
Number of toxicities seen at each dose.
Toxicity rate limit
Target toxicity rate
Binary toxicity outcomes.
Duration of trials.
Demand that a rescue dose is tried before stopping is permitted.
Make untested and unrecommended doses inadmissible.
Utility score of each dose.
Outcome weights.
Methods for working with dose-finding clinical trials. We provide implementations of many dose-finding clinical trial designs, including the continual reassessment method (CRM) by O'Quigley et al. (1990) <doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji et al. (2007) <doi:10.1177/1740774507079442>, the modified TPI (mTPI) design by Ji et al. (2010) <doi:10.1177/1740774510382799>, the Bayesian optimal interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the design of Wages & Tait (2015) <doi:10.1080/10543406.2014.920873>, and the 3+3 described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs are implemented with a common interface. We also offer optional additional classes to tailor the behaviour of all designs, including avoiding skipping doses, stopping after n patients have been treated at the recommended dose, stopping when a toxicity condition is met, or demanding that n patients are treated before stopping is allowed. By daisy-chaining together these classes using the pipe operator from 'magrittr', it is simple to tailor the behaviour of a dose-finding design so it behaves how the trialist wants. Having provided a flexible interface for specifying designs, we then provide functions to run simulations and calculate dose-paths for future cohorts of patients.
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