Calculate dose-transition pathways for an EffTox study

Source: R/efftox_dtps.R

Calculate dose-transition pathways for an EffTox study. The function efftox_dtps_to_dataframe performs a similar function, but is much less-flexible.

efftox_dtps(
  cohort_sizes,
  previous_outcomes = "",
  next_dose = NULL,
  user_dose_func = NULL,
  verbose = FALSE,
  i_am_patient = FALSE,
  ...
)

Arguments

cohort_sizes

vector of future cohort sizes, i.e. positive integers. E.g. To calculate paths for the the next cohort of two followed by another cohort of three, use cohort_sizes = c(2, 3).
previous_outcomes

Outcomes observed hitherto in the syntax required by efftox_parse_outcomes.
next_dose

the dose-level to be given to the immediately next cohort.
user_dose_func

optional delegate for deciding dose. A function that takes a efftox_fit as the sole argument and returns the integer (1-based) dose-level to be given next, or NA to show that no dose should be chosen and the trial stopped. This function gives the user the opportunity to build in custom behaviour to tailor the dose selection decision in response to the insights garnered by the fit model, or recommend that a trial path be halted immediately. If omitted, the dose ordinarily chosen by the model is used. An example is given below.
verbose

logical, TRUE to get progress messages.
i_am_patient

logical, TRUE to show your tolerance for waiting for over 100 models to fit. Set to FALSE by default.
...

extra params passed to rstan::sampling.

Value

dose pathways in a data.frame.

References

Yap C, Billingham LJ, Cheung YK, Craddock C, O’Quigley J. Dose transition pathways: The missing link between complex dose-finding designs and simple decision-making. Clinical Cancer Research. 2017;23(24):7440-7447. doi:10.1158/1078-0432.CCR-17-0582

Brock K, Billingham L, Copland M, Siddique S, Sirovica M, Yap C. Implementing the EffTox dose-finding design in the Matchpoint trial. BMC Medical Research Methodology. 2017;17(1):112. doi:10.1186/s12874-017-0381-x

See also

efftox_parse_outcomes, stan_efftox, efftox_path_analysis, dose_finding_path_node

Examples

if (FALSE) {
# Calculate paths for the first cohort of 3 in Thall et al 2014 example
paths1 <- efftox_dtps(cohort_sizes = c(3), next_dose = 1,
                      real_doses = c(1.0, 2.0, 4.0, 6.6, 10.0),
                      efficacy_hurdle = 0.5, toxicity_hurdle = 0.3,
                      p_e = 0.1, p_t = 0.1,
                      eff0 = 0.5, tox1 = 0.65,
                      eff_star = 0.7, tox_star = 0.25,
                      alpha_mean = -7.9593, alpha_sd = 3.5487,
                      beta_mean = 1.5482, beta_sd = 3.5018,
                      gamma_mean = 0.7367, gamma_sd = 2.5423,
                      zeta_mean = 3.4181, zeta_sd = 2.4406,
                      eta_mean = 0, eta_sd = 0.2,
                      psi_mean = 0, psi_sd = 1, seed = 123)



# Calculate paths for the next two cohorts of 2, in an in-progress trial
# Warning: this create 100 paths. It will run for a minute or two.
paths2 <- efftox_dtps(cohort_sizes = c(2, 2),
                      previous_outcomes = '1NN 2EE',
                      next_dose = 1,
                      real_doses = c(1.0, 2.0, 4.0, 6.6, 10.0),
                      efficacy_hurdle = 0.5, toxicity_hurdle = 0.3,
                      p_e = 0.1, p_t = 0.1,
                      eff0 = 0.5, tox1 = 0.65,
                      eff_star = 0.7, tox_star = 0.25,
                      alpha_mean = -7.9593, alpha_sd = 3.5487,
                      beta_mean = 1.5482, beta_sd = 3.5018,
                      gamma_mean = 0.7367, gamma_sd = 2.5423,
                      zeta_mean = 3.4181, zeta_sd = 2.4406,
                      eta_mean = 0, eta_sd = 0.2,
                      psi_mean = 0, psi_sd = 1, seed = 123,
                      i_am_patient = TRUE)

# Paths can be converted to a tibble
library(tibble)
library(dplyr)
df <- as_tibble(paths2)
df %>% print(n = 200)

# And shaped in a wide format
spread_paths(df %>% select(-fit, -parent_fit, -dose_index)) %>%
  print(n = 100)
# Incredibly, there are 100 ways these two cohorts of two can end up.



# An example with a custom dose selection function.
# Define a function to select the maximal utility dose, no matter what.
# Note: this diverges from the original authors' intentions; we provide this
# for illustration only!
max_utility_dose <- function(efftox_fit) {
  return(which.max(efftox_fit$utility))
}
# Fit the paths, providing the user_dose_func parameter
# Warning: this create 100 paths. It will run for a minute or two.
paths3 <- efftox_dtps(cohort_sizes = c(2, 2),
                      previous_outcomes = '1NN 2EE',
                      next_dose = 1,
                      real_doses = c(1.0, 2.0, 4.0, 6.6, 10.0),
                      efficacy_hurdle = 0.5, toxicity_hurdle = 0.3,
                      p_e = 0.1, p_t = 0.1,
                      eff0 = 0.5, tox1 = 0.65,
                      eff_star = 0.7, tox_star = 0.25,
                      alpha_mean = -7.9593, alpha_sd = 3.5487,
                      beta_mean = 1.5482, beta_sd = 3.5018,
                      gamma_mean = 0.7367, gamma_sd = 2.5423,
                      zeta_mean = 3.4181, zeta_sd = 2.4406,
                      eta_mean = 0, eta_sd = 0.2,
                      psi_mean = 0, psi_sd = 1,
                      user_dose_func = max_utility_dose,
                      seed = 123, i_am_patient = TRUE)

# We can see where the dose-selections differ at the second future cohort
# by joining these paths to those calculated in the previous example:
left_join(
  as_tibble(paths2)%>%
    select(.node, .parent, .depth, outcomes, model_dose = next_dose),
  as_tibble(paths3) %>%
    select(.node, user_dose = next_dose),
  by = '.node'
) %>% spread_paths() %>%
  filter(model_dose2 != user_dose2)
# They differ in many places. The user defined functions sometimes selects
# higher doses; sometimes lower.
}