RLoptimal

Overview

The purpose of this RLoptimal package is to easily construct an adaptive allocation rule that directly optimizes a performance metric, such as power, accuracy of the estimated target dose, or mean absolute error over the estimated dose-response curve. Several high-level functions are also provided to make it easy to perform simulation studies.

Installation

You can install the stable version from CRAN as follows.

install.packages("RLoptimal")

You can install the development version from GitHub as follows.

# install.packages("remotes")
remotes::install_github("MatsuuraKentaro/RLoptimal")

Example

We demonstrate computing an optimal adaptive allocation by reinforcement learning for the example in Section 3 of the original paper.

When you load RLoptimal as follows, Python itself and the Python packages to conduct reinforcement learning will be installed.

library(RLoptimal)

Build dose-response models for MCPMod

We build the dose-response models to be used in the MCPMod method, which we plan to execute at the end of the clinical trial.

doses <- c(0, 2, 4, 6, 8)

models <- DoseFinding::Mods(
  doses = doses, maxEff = 1.65,
  linear = NULL, emax = 0.79, sigEmax = c(4, 5)
)

Learn an allocation rule

We obtain an optimal adaptive allocation rule by executing learn_allocation_rule() with the models.

allocation_rule <- learn_allocation_rule(
  models,
  N_total = 150, N_ini = rep(10, 5), N_block = 10, Delta = 1.3,
  outcome_type = "continuous", sd_normal = sqrt(4.5), 
  seed = 123, rl_config = rl_config_set(iter = 1000),
  alpha = 0.025
)

allocation_rule
#> <AllocationRule>
#> dir: allocation_rules/20241012_065853
#> created at: 2024-10-12 10:18:51
#> call:
#> learn_allocation_rule(models = models, N_total = 150, N_ini = rep(10, 
#>     5), N_block = 10, Delta = 1.3, outcome_type = "continuous", 
#>     sd_normal = sqrt(4.5), seed = 123, rl_config = rl_config_set(iter = 1000), 
#>     alpha = 0.025)
#> iterations: 1000
#> checkpoints: 500, 600, 700, 800, 900, 1000

With the default settings, it takes roughly 10-50 seconds per iter, so it would take about 3-14 hours when iter = 1000.

How to use the allocation rule

To compute allocation ratios using the obtained allocation rule, pass dose and response data to opt_allocation_probs().

some_doses <- c( 0,  0,  0,  0,  2,  2,  4,  4,  4,  6,  6,   8,  8,   8)
some_resps <- c(.2, .1, .0, .3, .2, .4, .1, .6, .8, .5, .8, 1.1, .9, 1.6)

allocation_rule$opt_allocation_probs(some_doses, some_resps)
#>            0            2            4            6            8 
#> 3.506682e-01 5.533118e-08 6.740401e-02 2.543487e-05 5.819023e-01

When 10 subjects in the next block are allocated to each dose according to these probabilities, we recommend using DoseFinding::rndDesign().

probs <- allocation_rule$opt_allocation_probs(some_doses, some_resps)
DoseFinding::rndDesign(probs, 10)
#> [1] 4 0 1 0 5

Simulation-based adjustment of the significance level

In general, an adaptive allocation may inflate alpha (see Section 3.3 in the original paper). Therefore, the significance level should be adjusted by simulation using adjust_significance_level function.

adjusted_alpha <- adjust_significance_level(
  allocation_rule, models,
  N_total = 150, N_ini = rep(10, 5), N_block = 10,
  outcome_type = "continuous", sd_normal = sqrt(4.5),
  alpha = 0.025, n_sim = 10000, seed = 123
)

adjusted_alpha
#> [1] 0.0221804

How to evaluate the allocation rule

A convenient high-level function (simulate_one_trial) is provided to evaluate the obtained allocation rule. The following is an example of code to perform a simulation study similar to Section 3 in the original paper.

eval_models <- DoseFinding::Mods(
  doses = doses, maxEff = 1.65,
  linear = NULL, emax = 0.79, sigEmax = c(4, 5), exponential = 1, quadratic = - 1/12
)
true_response_matrix <- DoseFinding::getResp(eval_models, doses = doses)
true_response_list <- as.list(data.frame(true_response_matrix, check.names = FALSE))

n_sim <- 1000  # the number of simulated clinical trials
sim_list <- list()

for (true_model_name in names(true_response_list)) {
  true_response <- true_response_list[[true_model_name]]
  for (simID in seq_len(n_sim)) {
    sim_one <- simulate_one_trial(
      allocation_rule, models, 
      true_response = true_response,
      N_total = 150, N_ini = rep(10, 5), N_block = 10, 
      Delta = 1.3, outcome_type = "continuous", sd_normal = sqrt(4.5),
      alpha = adjusted_alpha, seed = simID, eval_type = "all"
    )
    sim_list[[length(sim_list) + 1]] <- data.frame(
      simID = simID, true_model_name = true_model_name, sim_one, check.names = FALSE)
  }
}

d_sim <- do.call(rbind, sim_list)
head(d_sim, 10)
#>    simID true_model_name  min_p_value selected_model_name estimated_target_dose         MAE    n_of_0    n_of_2     n_of_4     n_of_6    n_of_8
#> 1      1          linear 3.637147e-04              linear              6.219219 0.013897321 0.3066667 0.3866667 0.06666667 0.10000000 0.1400000
#> 2      2          linear 1.788856e-03              linear              6.523529 0.034856764 0.3933333 0.2400000 0.06666667 0.16666667 0.1333333
#> 3      3          linear 2.992331e-07              linear              5.812620 0.087006441 0.3533333 0.1133333 0.06666667 0.06666667 0.4000000
#> 4      4          linear 3.650607e-03              linear              8.392319 0.256732278 0.3600000 0.1733333 0.20666667 0.12666667 0.1333333
#> 5      5          linear 2.845267e-04              linear              6.240667 0.010305335 0.3600000 0.2666667 0.16000000 0.06666667 0.1466667
#> 6      6          linear 3.881463e-03              linear              8.081026 0.226896732 0.3400000 0.3000000 0.06666667 0.12666667 0.1666667
#> 7      7          linear 4.219836e-06              linear              5.251137 0.206577181 0.3866667 0.2533333 0.06666667 0.09333333 0.2000000
#> 8      8          linear 4.519901e-02                <NA>                    NA          NA 0.2933333 0.4600000 0.06666667 0.07333333 0.1066667
#> 9      9          linear 3.358248e-03              linear              6.262230 0.006718837 0.2466667 0.4866667 0.10000000 0.06666667 0.1000000
#> 10    10          linear 1.302790e-08              linear              4.689112 0.354939835 0.2666667 0.1466667 0.06666667 0.13333333 0.3866667

Tips

Dose-response models for reinforcement learning

It is recommended that the models used in reinforcement learning include possible models in addition to the models used in the MCPMod method. Here, we add the exponential model according to the supporting information in the original paper, and specify the argument rl_models in learn_allocation_rule function.

rl_models <- DoseFinding::Mods(
  doses = doses, maxEff = 1.65,
  linear = NULL, emax = 0.79, sigEmax = c(4, 5), exponential = 1
)

allocation_rule <- learn_allocation_rule(
  models,
  N_total = 150, N_ini = rep(10, 5), N_block = 10, Delta = 1.3,
  outcome_type = "continuous", sd_normal = sqrt(4.5), 
  seed = 123, rl_models = rl_models, rl_config = rl_config_set(iter = 1000),
  alpha = 0.025
)

Binary outcome

The above workflow can be applied in the same way when the outcome is binary. We build the dose-response models to be used in the MCPMod method on the logit scale (see this vignette of DoseFinding package), and specify the argument outcome_type = "binary" in learn_allocation_rule function.

doses <- c(0, 0.5, 1.5, 2.5, 4)

models <- DoseFinding::Mods(
  doses = doses, 
  placEff = qlogis(0.1), 
  maxEff = qlogis(0.35) - qlogis(0.1),
  emax = c(0.25, 1), sigEmax = rbind(c(1, 3), c(2.5, 4)), betaMod = c(1.1, 1.1)
)

allocation_rule <- learn_allocation_rule(
  models,
  N_total = 200, N_ini = rep(10, 5), N_block = 10,
  Delta = 1.4, outcome_type = "binary",
  seed = 123, rl_config = rl_config_set(iter = 1000),
  alpha = 0.05
)

How to use Allocation Rule Class

The allocation_rule above is an object of the Allocation Rule Class (R6). Here is a brief explanation of how to use it.

Save the allocation rule

The obtained allocation rule can be saved using saveRDS, a standard R function.

saveRDS(allocation_rule, file = "allocation_rule.RDS")

To load it, use readRDS.

allocation_rule <- readRDS(file = "allocation_rule.RDS")

Inputs of `learn_allocation_rule` function

The inputs passed to the learn_allocation_rule function can be retrieved as follows.

allocation_rule$input

Obtain returns during reinforcement learning

The statistics of returns during reinforcement learning can be retrieved as follows.

allocation_rule$log

Resume learning

Reinforcement learning can be resumed with the following function.

allocation_rule$resume_learning(iter = 100)

Use checkpoint

Multiple checkpoints are created by learn_allocation_rule function. By default, the last checkpoint is used to build an allocation rule. If you want to build another allocation rule using another checkpoint, specify the directory name created by learn_allocation_rule function as follows.

another_allocation_rule <- AllocationRule$new(dir = "checkpoints/20241012_065853_00900")

Optimal Adaptive Allocation Using Deep Reinforcement Learning