Using escalation
In escalation: Modular Approach to Dose Finding Clinical Trials

The escalation package by Kristian Brock. Documentation is hosted at https://brockk.github.io/escalation/

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Introduction

To provide dose selection decisions, the escalation package daisy-chains together objects that support a common interface, each deriving from type selector. This vignette demonstrates the entire interface supported by selector objects. For the purpose of illustratration, we use a BOIN selector but the same functions will work on every type of dose selector in escalation.

library(escalation)

model <- get_boin(num_doses = 5, target = 0.3)
fit <- model %>% fit('1NN 2NN 3NT 2NT')

Supported Interface

Target toxicity rate:

tox_target(fit)

The number of patients treated:

num_patients(fit)

Cohort IDs for the treated patients:

cohort(fit)

The code infers from the spaces in the outcome string that a dose-decision was made after the second, fourth , and sixth patients.

Integers representing the dose-levels given:

doses_given(fit)

Bits representing whether toxicity event was observed:

tox(fit)

The total number of toxicities seen at all doses combined:

num_tox(fit)

A data-frame containing the above information:

model_frame(fit)

The number of doses under investigation:

num_doses(fit)

The indices of the dose-levels under investigation:

dose_indices(fit)

The dose-level recommended for the next patient:

recommended_dose(fit)

After seeing some toxicity at doses 2 and 3, the design sensibly sticks at dose 2 for the time being.

A logical value for whether accrual should continue:

continue(fit)

We infer from this that no stopping condition has yet been triggered.

The number of patients treated at each dose:

n_at_dose(fit)

The number of patients treated at the recommended dose:

n_at_recommended_dose(fit)

The proportion of patients treated at each dose:

prob_administer(fit)

The total number of toxicities seen at each dose:

tox_at_dose(fit)

The empirical toxicity rate, i.e. the number of toxicities divided by the number of patients:

empiric_tox_rate(fit)

The model-derived expected toxicity rate at each dose:

mean_prob_tox(fit)

The BOIN design makes no estimate for doses it has not yet administered.

The model-derived median toxicity rate at each dose:

median_prob_tox(fit)

BOIN does not actually calculate posterior median estimates. Sometimes it will be necessary to return missing values if functionality is not supported by a model. Median estimates could be added to the BOIN class in due course.

The model-derived quantile of the toxicity rate at each dose:

prob_tox_quantile(fit, 0.9)

BOIN does not calculate this either. It could also be added.

The posterior probability that the toxicity rate exceeds some threshold value, here 50%:

prob_tox_exceeds(fit, 0.5)

Once again, no estimate is made for non-administered doses. We see that the model estimates a trivial chance that the toxicity rate at the lowest dose exceeds 50%.

Learn if this model supports sampling from the posterior: