simulations | R Documentation |
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 selector
s, 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, ...)
fits |
Simulated model fits, arranged as list of lists. |
true_prob_tox |
vector of true toxicity probabilities |
true_prob_eff |
vector of true efficacy probabilities, optionally NULL if efficacy not analysed. |
... |
Extra args |
The simulations
object implements the following functions:
num_patients
num_doses
dose_indices
n_at_dose
tox_at_dose
num_tox
recommended_dose
prob_administer
prob_recommend
trial_duration
list with slots: fits
containing model fits;
and true_prob_tox
, contianing the assumed true probability of
toxicity.
selector
simulate_trials
# 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()
sims %>% num_doses()
sims %>% dose_indices()
sims %>% n_at_dose()
sims %>% tox_at_dose()
sims %>% num_tox()
sims %>% recommended_dose()
sims %>% prob_administer()
sims %>% prob_recommend()
sims %>% trial_duration()
# Access the list of model fits for the ith simulated trial using:
i <- 1
sims$fits[[i]]
# and the jth model fit for the ith simulated trial using:
j <- 1
sims$fits[[i]][[j]]
# and so on.
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