This class encapsulates that many notional or virtual trials can be simulated. Each recommends a dose (or doses), keeps track of how many patients have been treated at what doses, what toxicity outcomes have been seen, and whether a trial advocates continuing, etc. We run simulations to learn about the operating characteristics of a trial design.
Computationally, the simulations class supports much of the same
interface as selector, and a little more.
Thus, many of the same generic functions are supported - see Examples.
However, compared to selectors, the returned objects reflect
that there are many trials instead of one, e.g. num_patients(sims),
returns as an integer vector the number of patients used in the simulated
trials.
simulations(fits, true_prob_tox, true_prob_eff = NULL, ...)Simulated model fits, arranged as list of lists.
vector of true toxicity probabilities
vector of true efficacy probabilities, optionally NULL if efficacy not analysed.
Extra args
list with slots: fits containing model fits;
and true_prob_tox, contianing the assumed true probability of
toxicity.
The simulations object implements the following functions:
# Simulate performance of the 3+3 design:
true_prob_tox <- c(0.12, 0.27, 0.44, 0.53, 0.57)
sims <- get_three_plus_three(num_doses = 5) %>%
simulate_trials(num_sims = 10, true_prob_tox = true_prob_tox)
# The returned object has type 'simulations'. The supported interface is:
sims %>% num_patients()
#> [1] 9 12 6 9 9 6 9 6 12 12
sims %>% num_doses()
#> [1] 5
sims %>% dose_indices()
#> [1] 1 2 3 4 5
sims %>% n_at_dose()
#> # A tibble: 10 × 5
#> `1` `2` `3` `4` `5`
#> <int> <int> <int> <int> <int>
#> 1 3 3 3 0 0
#> 2 3 3 6 0 0
#> 3 3 3 0 0 0
#> 4 3 6 0 0 0
#> 5 6 3 0 0 0
#> 6 6 0 0 0 0
#> 7 3 6 0 0 0
#> 8 3 3 0 0 0
#> 9 3 6 3 0 0
#> 10 6 6 0 0 0
sims %>% tox_at_dose()
#> # A tibble: 10 × 5
#> `1` `2` `3` `4` `5`
#> <int> <int> <int> <int> <int>
#> 1 0 0 2 0 0
#> 2 0 0 3 0 0
#> 3 0 2 0 0 0
#> 4 0 2 0 0 0
#> 5 1 2 0 0 0
#> 6 2 0 0 0 0
#> 7 0 2 0 0 0
#> 8 0 3 0 0 0
#> 9 0 1 2 0 0
#> 10 1 2 0 0 0
sims %>% num_tox()
#> [1] 2 3 2 2 3 2 2 3 3 3
sims %>% recommended_dose()
#> [1] 2 2 1 1 1 NA 1 1 2 1
sims %>% prob_administer()
#> 1 2 3 4 5
#> 0.4333333 0.4333333 0.1333333 0.0000000 0.0000000
sims %>% prob_recommend()
#> NoDose 1 2 3 4 5
#> 0.1 0.6 0.3 0.0 0.0 0.0
sims %>% trial_duration()
#> [1] 4.2 11.9 5.2 9.4 9.1 3.1 9.0 3.7 14.8 17.0
# Access the list of model fits for the ith simulated trial using:
i <- 1
sims$fits[[i]]
#> [[1]]
#> [[1]]$.depth
#> [1] 4
#>
#> [[1]]$time
#> [1] 4.2
#>
#> [[1]]$fit
#> Patient-level data:
#> # A tibble: 9 × 4
#> Patient Cohort Dose Tox
#> <int> <int> <int> <int>
#> 1 1 1 1 0
#> 2 2 1 1 0
#> 3 3 1 1 0
#> 4 4 2 2 0
#> 5 5 2 2 0
#> 6 6 2 2 0
#> 7 7 3 3 0
#> 8 8 3 3 1
#> 9 9 3 3 1
#>
#> Dose-level data:
#> # A tibble: 6 × 8
#> dose tox n empiric_tox_rate mean_prob_tox median_prob_tox admissible
#> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
#> 1 NoDose 0 0 0 0 0 TRUE
#> 2 1 0 3 0 NA NA TRUE
#> 3 2 0 3 0 NA NA TRUE
#> 4 3 2 3 0.667 NA NA FALSE
#> 5 4 0 0 NaN NA NA FALSE
#> 6 5 0 0 NaN NA NA FALSE
#> # ℹ 1 more variable: recommended <lgl>
#>
#> The model advocates stopping and recommending dose 2.
#>
#>
# and the jth model fit for the ith simulated trial using:
j <- 1
sims$fits[[i]][[j]]
#> $.depth
#> [1] 4
#>
#> $time
#> [1] 4.2
#>
#> $fit
#> Patient-level data:
#> # A tibble: 9 × 4
#> Patient Cohort Dose Tox
#> <int> <int> <int> <int>
#> 1 1 1 1 0
#> 2 2 1 1 0
#> 3 3 1 1 0
#> 4 4 2 2 0
#> 5 5 2 2 0
#> 6 6 2 2 0
#> 7 7 3 3 0
#> 8 8 3 3 1
#> 9 9 3 3 1
#>
#> Dose-level data:
#> # A tibble: 6 × 8
#> dose tox n empiric_tox_rate mean_prob_tox median_prob_tox admissible
#> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
#> 1 NoDose 0 0 0 0 0 TRUE
#> 2 1 0 3 0 NA NA TRUE
#> 3 2 0 3 0 NA NA TRUE
#> 4 3 2 3 0.667 NA NA FALSE
#> 5 4 0 0 NaN NA NA FALSE
#> 6 5 0 0 NaN NA NA FALSE
#> # ℹ 1 more variable: recommended <lgl>
#>
#> The model advocates stopping and recommending dose 2.
#>
# and so on.