matsuurakentaro / optimal_dose_escalation_in_phase_i_oncology_trials

Supplementary Materials for Kentaro Matsuura, Kentaro Sakamaki, Junya Honda, Takashi Sozu "Optimal Dose Escalation Methods using Deep Reinforcement Learning in Phase I Oncology Trials" Journal of Biopharmaceutical Statistics 2022

Optimal Dose Escalation Methods using Deep Reinforcement Learning in Phase I Oncology Trials

Supplementary Materials for Kentaro Matsuura, Kentaro Sakamaki, Junya Honda, Takashi Sozu "Optimal Dose Escalation Methods using Deep Reinforcement Learning in Phase I Oncology Trials" Journal of Biopharmaceutical Statistics 2022; (doi:xxxx)

How to Setup

We recommend using Linux or WSL on Windows, because the Ray package in Python is more stable on Linux. For example, in Ubuntu 20.04 (Python 3.8 was already installed), I was able to install the necessary packages with the following commands.

Install Ray

sudo apt update
sudo apt upgrade
sudo apt install python3-pip
sudo pip3 install torch
sudo pip3 install -U ray

Install R and RPy2

To install R, see https://cran.r-project.org/bin/linux/ubuntu/

sudo pip3 install rpy2

Install DoseFinding package in R

install.packages('DoseFinding')

How to Use

Change simulation settings

To change the simulation settings, it is necessary to understand RLE/envs/RLEEnv.py. This part is a bit difficult. Therefore, we have a plan to create an R package to use our method easily.

Obtain adaptive allocation rule

To obtain RLE by learning, please run learn_RLE.py like:

nohup python3 learn_RLE.py > std.log 2> err.log &

When we used c2-standard-4 (vCPUx4, RAM16GB) on Google Cloud Platform, the learning was completed within a day.

Simulate single trial

After the learning, we will obtain a checkpoint in ~/ray_results/PPO_RLE-v0_[datetime]-[xxx]/checkpoint-[yyy]/. To simulate single trial using the obtained rule, please move the checkpoint directory (checkpoint-[yyy]) to checkpoint/ in this repository, and edit the path in simulate-single-trial_RLE.py. Then, please run it:

python3 simulate-single-trial_RLE.py