escalation package by Kristian Brock. Documentation is hosted at https://brockk.github.io/escalation/
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
Target toxicity rate:
The number of patients treated:
Cohort IDs for the treated patients:
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:
Bits representing whether toxicity event was observed:
The total number of toxicities seen at all doses combined:
A data-frame containing the above information:
The number of doses under investigation:
The indices of the dose-levels under investigation:
The dose-level recommended for the next patient:
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:
We infer from this that no stopping condition has yet been triggered.
The number of patients treated at each dose:
The number of patients treated at the recommended dose:
The proportion of patients treated at each dose:
The total number of toxicities seen at each dose:
The empirical toxicity rate, i.e. the number of toxicities divided by the number of patients:
The model-derived expected toxicity rate at each dose:
The BOIN design makes no estimate for doses it has not yet administered.
The model-derived median toxicity rate at each dose:
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:
BOIN does not calculate this either. It could also be added.
The posterior probability that the toxicity rate exceeds some threshold value, here 50%:
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:
The BOIN model does not support sampling. If it did, we could run
We can also call some standard generic functions:
print(fit) #> Patient-level data: #> # A tibble: 8 x 4 #> Patient Cohort Dose Tox #> <int> <int> <int> <int> #> 1 1 1 1 0 #> 2 2 1 1 0 #> 3 3 2 2 0 #> 4 4 2 2 0 #> 5 5 3 3 0 #> 6 6 3 3 1 #> 7 7 4 2 0 #> 8 8 4 2 1 #> #> Dose-level data: #> # A tibble: 5 x 6 #> dose tox n empiric_tox_rate mean_prob_tox median_prob_tox #> <int> <int> <int> <dbl> <dbl> <dbl> #> 1 1 0 2 0 0.02 0.01 #> 2 2 1 4 0.25 0.26 0.21 #> 3 3 1 2 0.5 0.5 0.5 #> 4 4 0 0 NaN NA NA #> 5 5 0 0 NaN NA NA #> #> The model targets a toxicity level of 0.3. #> The model advocates continuing at dose 2.
and cast it to a tidyverse
library(tibble) as_tibble(fit) #> # A tibble: 6 x 7 #> dose tox n empiric_tox_rate mean_prob_tox median_prob_tox recommended #> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> #> 1 NoDose 0 0 0 0 0 FALSE #> 2 1 0 2 0 0.02 0.01 FALSE #> 3 2 1 4 0.25 0.26 0.21 TRUE #> 4 3 1 2 0.5 0.5 0.5 FALSE #> 5 4 0 0 NaN NA NA FALSE #> 6 5 0 0 NaN NA NA FALSE