simulate_compare: Simulate clinical trials for several designs using common...
In escalation: A Modular Approach to Dose-Finding Clinical Trials

simulate_compare

R Documentation

Simulate clinical trials for several designs using common patients.

Description

This 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.

Usage

simulate_compare(
  designs,
  num_sims,
  true_prob_tox,
  true_prob_eff = NULL,
  patient_samples = NULL,
  rho = NULL,
  return_patient_samples = FALSE,
  ...
)

Arguments

`designs`	list, mapping design names to objects of type `selector_factory`.
`num_sims`	integer, number of trial iterations to simulate.
`true_prob_tox`	numeric vector of true but unknown toxicity probabilities
`true_prob_eff`	numeric vector of true but unknown efficacy probabilities. NULL if efficacy not analysed.
`patient_samples`	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.
`rho`	Optional correlation between -1 and 1 for the latent uniform variables that determine toxicity and efficacy events. Non-correlated events is the default.
`return_patient_samples`	TRUE to get the list of patient sample objects returned in the patient_samples attribute of the retured object.
`...`	Extra args are passed onwards.

Details

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.

Value

object of type simulations_collection

References

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

Examples

## Not run: 
# 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
)

## End(Not run)

escalation documentation built on June 27, 2024, 5:09 p.m.