Working with dose selectors

Supported Interface

Target toxicity rate:

tox_target(fit)
#> [1] 0.3

The number of patients treated:

num_patients(fit)
#> [1] 8

Cohort IDs for the treated patients:

cohort(fit)
#> [1] 1 1 2 2 3 3 4 4

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)
#> [1] 1 1 2 2 3 3 2 2

Bits representing whether toxicity event was observed:

tox(fit)
#> [1] 0 0 0 0 0 1 0 1

The total number of toxicities seen at all doses combined:

num_tox(fit)
#> [1] 2

A data-frame containing the above information:

model_frame(fit)
#> # 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

The number of doses under investigation:

num_doses(fit)
#> [1] 5

The indices of the dose-levels under investigation:

dose_indices(fit)
#> [1] 1 2 3 4 5

The dose-level recommended for the next patient:

recommended_dose(fit)
#> [1] 2

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)
#> [1] TRUE

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

The total number of patients treated at each dose:

n_at_dose(fit)
#> [1] 2 4 2 0 0

The proportion of patients treated at each dose:

prob_administer(fit)
#>    1    2    3    4    5 
#> 0.25 0.50 0.25 0.00 0.00

The total number of toxicities seen at each dose:

tox_at_dose(fit)
#> [1] 0 1 1 0 0

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

empiric_tox_rate(fit)
#> [1] 0.00 0.25 0.50  NaN  NaN

The model-derived expected toxicity rate at each dose:

mean_prob_tox(fit)
#> [1] 0.02 0.26 0.50   NA   NA

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)
#>    1    2    3    4    5 
#> 0.01 0.21 0.50   NA   NA

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)
#>    1    2    3    4    5 
#> 0.05 0.54 0.89   NA   NA

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)
#> [1] 0.009391185 0.129051871 0.500000000          NA          NA

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:

supports_sampling(fit)
#> [1] FALSE

The BOIN model does not support sampling. If it did, we could run prob_tox_samples(fit).