R/simulate_compare.R
simulate_compare.RdThis function takes a list of several
selector_factorys, such as those returned by
get_dfcrm, get_boin or
get_three_plus_three, and conducts many notional clinical
trials. The simulated patients in the trials are common across designs. For
example, in a comparison of the three designs mentioned above, the first
simulated CRM trial uses the same notional patients as the first simulated
BOIN trial, etc. Using common patients within iterate across designs
reduces MCMC errors of comparisons, so this method is efficient for
comparing designs. See Sweeting et al. for full details.
simulate_compare(
designs,
num_sims,
true_prob_tox,
true_prob_eff = NULL,
patient_samples = NULL,
rho = NULL,
return_patient_samples = FALSE,
...
)list, mapping design names to objects of type
selector_factory.
integer, number of trial iterations to simulate.
numeric vector of true but unknown toxicity probabilities
numeric vector of true but unknown efficacy probabilities. NULL if efficacy not analysed.
Optional list of length num_sims, where each
element is an instance of PatientSample or a subclass like
CorrelatedPatientSample. These objects control the occurrence
of toxicity and efficacy events in patients. They are specifiable to allow
fine-grained control to users. See the vignette on Simulation.
Optional correlation between -1 and 1 for the latent uniform variables that determine toxicity and efficacy events. Non-correlated events is the default.
TRUE to get the list of patient sample objects returned in the patient_samples attribute of the retured object.
Extra args are passed onwards.
object of type simulations_collection
By default, dose decisions in simulated trials are made after each
cohort of 3 patients. This can be changed by providing a function by the
sample_patient_arrivals parameter that simulates the arrival of new
patients. The new patients will be added to the existing patients and the
model will be fit to the set of all patients. The function that simulates
patient arrivals should take as a single parameter a data-frame with one
row for each existing patient and columns including cohort, patient, dose,
tox, time (and possibly also eff and weight, if a phase I/II or
time-to-event method is used). The provision of data on the existing
patients allows the patient sampling function to be adaptive. The function
should return a data-frame with a row for each new patient and a column for
time_delta, the time between the arrival of this patient and the previous,
as in cohorts_of_n. See Examples.
This method can simulate the culmination of trials that are partly
completed. We just have to specify the outcomes already observed via the
previous_outcomes parameter. Each simulated trial will commence from
those outcomes seen thus far. See Examples.
We can specify the immediate next dose by specifying next_dose. If
omitted, the next dose is calculated by invoking the model on the outcomes
seen thus far.
Designs must eventually choose to stop the trial. Some designs, like 3+3,
have intrinsic stopping rules. However, some selectors like those derived
from get_dfcrm offer no default stopping method. You may need
to append stopping behaviour to your selector via something like
stop_at_n or stop_when_n_at_dose, etc. To
safeguard against simulating runaway trials that never end, the function
will halt a simulated trial after 30 invocations of the dose-selection
decision. To breach this limit, specify i_like_big_trials = TRUE in
the function call. However, when you forego the safety net, the onus is on
you to write selectors that will eventually stop the trial! See Examples.
The model is fit to the prevailing data at each dose selection point. By
default, only the final model fit for each simulated trial is retained.
This is done to conserve memory. With a high number of simulated trials,
storing many model fits per trial may cause the executing machine to run
out of memory. However, you can force this method to retain all model fits
by specifying return_all_fits = TRUE. See Examples.
Sweeting, M., Slade, D., Jackson, D., & Brock, K. (2024). Potential outcome simulation for efficient head-to-head comparison of adaptive dose-finding designs. arXiv preprint arXiv:2402.15460
if (FALSE) { # \dontrun{
# Don't run on build because they exceed CRAN time limit
# In a five-dose scenario, we have assumed probabilities for Prob(tox):
true_prob_tox <- c(0.05, 0.10, 0.15, 0.18, 0.45)
# and Prob(eff):
true_prob_eff <- c(0.40, 0.50, 0.52, 0.53, 0.53)
# Let us compare two BOIN12 variants that differ in their stopping params:
designs <- list(
"BOIN12 v1" = get_boin12(num_doses = 5,
phi_t = 0.35, phi_e = 0.25,
u2 = 40, u3 = 60,
c_t = 0.95, c_e = 0.9) %>%
stop_at_n(n = 36),
"BOIN12 v2" = get_boin12(num_doses = 5,
phi_t = 0.35, phi_e = 0.25,
u2 = 40, u3 = 60,
c_t = 0.5, c_e = 0.5) %>%
stop_at_n(n = 36)
)
# For illustration we run only 10 iterates:
x <- simulate_compare(
designs,
num_sims = 10,
true_prob_tox,
true_prob_eff
)
# To compare toxicity-only designs like CRM etc, we would omit true_prob_eff.
# We might be interested in the absolute dose recommendation probabilities:
convergence_plot(x)
library(dplyr)
library(ggplot2)
# and, perhaps more importantly, how they compare:
as_tibble(x) %>%
ggplot(aes(x = n, y = delta)) +
geom_point(size = 0.4) +
geom_linerange(aes(ymin = delta_l, ymax = delta_u)) +
geom_hline(yintercept = 0, linetype = "dashed", col = "red") +
facet_grid(comparison ~ dose,
labeller = labeller(
.rows = label_both,
.cols = label_both)
)
# Simulations for each design are available by name:
sims <- x$`BOIN12 v1`
# And the usual functions are available on the sims objects:
sims %>% num_patients()
sims %>% num_doses()
sims %>% dose_indices()
sims %>% n_at_dose()
# etc
# See ? simulate_trials
# As with simulate_trials, which examines one design, we also have options to
# tweak the simulation process.
# By default, dose decisions are made after each cohort of 3 patients. To
# override, specify an alternative function via the sample_patient_arrivals
# parameter. E.g. to use cohorts of 2, we run:
patient_arrivals_func <- function(current_data) cohorts_of_n(n = 2)
x <- simulate_compare(
designs,
num_sims = 10,
true_prob_tox,
true_prob_eff,
sample_patient_arrivals = patient_arrivals_func
)
# To simulate the culmination of trials that are partly completed, specify
# the outcomes already observed via the previous_outcomes parameter. Imagine
# one cohort has already been evaluated, returning outcomes 1NTN. We can
# simulate the remaining part of that trial with:
x <- simulate_compare(
designs,
num_sims = 10,
true_prob_tox,
true_prob_eff,
previous_outcomes = '1NTN'
)
# Outcomes can be described by the above outcome string method or data-frame:
previous_outcomes <- data.frame(
patient = 1:3,
cohort = c(1, 1, 1),
tox = c(0, 1, 0),
eff = c(1, 1, 0),
dose = c(1, 1, 1)
)
x <- simulate_compare(
designs,
num_sims = 10,
true_prob_tox,
true_prob_eff,
previous_outcomes = previous_outcomes
)
# We can specify the immediate next dose:
x <- simulate_compare(
designs,
num_sims = 10,
true_prob_tox,
true_prob_eff,
next_dose = 5
)
# By default, the method will stop simulated trials after 30 dose selections.
# To suppress this, specify i_like_big_trials = TRUE. However, please take
# care to specify selectors that will eventually stop! Our designs above use
# stop_at_n so they will not proceed ad infinitum.
x <- simulate_compare(
designs,
num_sims = 10,
true_prob_tox,
true_prob_eff,
i_like_big_trials = TRUE
)
# By default, only the final model fit is retained for each simulated trial.
# To retain all interim model fits, specify return_all_fits = TRUE.
x <- simulate_compare(
designs,
num_sims = 10,
true_prob_tox,
true_prob_eff,
return_all_fits = TRUE
)
} # }