BayesRegDTR

Optimal Dynamic Treatment Regime Inference using Bayesian Likelihood-Based Regression Method

About this package

This package provides methods to estimate optimal dynamic treatment regimes using Bayesian likelihood-based regression approach as described in Yu, W., & Bondell, H. D. (2023). Uses backward induction and dynamic programming theory for computing expected values. Offers options for future parallel computing.

Pre-installation instructions (Mac Users Only)

To install this package in Mac requires a Fortran compiler (through its RcppArmadillo dependency). Chances are, your current Fortran compiler is not up-to-date. To update your Fortran compiler, simply follow the steps here:
 

1. In your Mac App Store, search “Xcode” and install.
   
2. Open Terminal application. Type in

xcode-select --install

     and follow the instructions.
     3. Click on the link here. Download the gfortan dmg file according to your MacOS version.
     4. Open the dmg file, run the gfortran installer, follow all the instructions.

An alternative recommended method is to use the packet manager Homebrew:

  1. Check if you have homebrew with

$ brew doctor

     If you don’t have it installed, use the following code from the Homebrew webiste. Check the website that it hasn’t changed since. It will ask for your user password (you won’t see characters as you type). Follow the instructions.

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

     2. Install GFortran using gcc (contains GFortran).

brew install gcc

Installation

# Install the development version from GitHub:
install.packages("devtools")
devtools::install_github("jlimrasc/BayesRegDTR")

Toy example

library(BayesRegDTR)
# -----------------------------
# Set Up Parallelism & Progress Bar
# -----------------------------
progressr::handlers("cli")          # Set handler to something with title/text
numCores <- parallel::detectCores() # Detect number of cores, use max
future::plan(future::multisession,  # Or plan(multicore, workers) on Unix
         workers = numCores)        # Set number of cores to use
doFuture::registerDoFuture()        # Or doParallel::registerDoParallel()
                                    # if no progress bar is needed and future
                                    # is unwanted

## UVT
# -----------------------------
# Initialise Inputs
# -----------------------------
num_stages  <- 5
t           <- 3
p_list      <- rep(1, num_stages)
num_treats  <- rep(2, num_stages)
n.train     <- 5000
n.pred      <- 10
# -----------------------------
# Generate Dataset
# -----------------------------
Dat.train  <- generate_dataset(n.train,  num_stages, p_list, num_treats)
Dat.pred  <- generate_dataset(n.pred,  num_stages, p_list, num_treats)
Dat.pred  <- Dat.pred[-1]
Dat.pred[[num_stages+1]]  <- Dat.pred[[num_stages+1]][1:n.pred, 1:(t-1), drop = FALSE]

# -----------------------------
# Main
# -----------------------------
gcv_uvt <- BayesLinRegDTR.model.fit(Dat.train, Dat.pred, n.train, n.pred,
                                 num_stages, num_treats,
                                 p_list, t, R = 30,
                                 tau = 0.01, B = 500, nu0 = NULL,
                                 V0 = NULL, alph = 3, gam = 4)

## MVT
# -----------------------------
# Initialise Inputs
# -----------------------------
num_stages  <- 3
t           <- 2
p_list      <- rep(2, num_stages)
num_treats  <- rep(2, num_stages)
n.train     <- 5000
n.pred      <- 10

# -----------------------------
# Generate Dataset
# -----------------------------
Dat.train <- generate_dataset(n.train, num_stages, p_list, num_treats)
Dat.pred  <- generate_dataset(n.pred,  num_stages, p_list, num_treats)
Dat.pred  <- Dat.pred[-1]
Dat.pred[[num_stages+1]]  <- Dat.pred[[num_stages+1]][1:n.pred, 1:(t-1), drop = FALSE]

# -----------------------------
# Main
# -----------------------------
gcv_res <- BayesLinRegDTR.model.fit(Dat.train, Dat.pred, n.train, n.pred,
                                 num_stages, num_treats,
                                 p_list, t, R = 30,
                                 tau = 0.01, B = 500, nu0 = 3,
                                 V0 = mapply(diag, p_list, SIMPLIFY = FALSE),
                                 alph = 3, gam = 4)