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Type: Package
Title: Bayesian Regression for Dynamic Treatment Regimes
Version: 1.0.1
Description: Methods to estimate optimal dynamic treatment regimes using Bayesian likelihood-based regression approach as described in Yu, W., & Bondell, H. D. (2023) <doi:10.1093/jrsssb/qkad016> Uses backward induction and dynamic programming theory for computing expected values. Offers options for future parallel computing.
License: GPL (≥ 3)
Imports: Rcpp (≥ 1.0.13-1), mvtnorm, foreach, progressr, stats, future
Depends: doRNG
Suggests: cli, testthat (≥ 3.0.0), doFuture
LinkingTo: Rcpp, RcppArmadillo
Encoding: UTF-8
RoxygenNote: 7.3.2
URL: https://github.com/jlimrasc/BayesRegDTR
BugReports: https://github.com/jlimrasc/BayesRegDTR/issues
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2025-06-24 10:16:44 UTC; jerem
Author: Jeremy Lim [aut, cre], Weichang Yu ORCID iD [aut]
Maintainer: Jeremy Lim <jeremylim23@gmail.com>
Repository: CRAN
Date/Publication: 2025-06-27 13:20:02 UTC

BayesRegDTR: Bayesian Regression for Dynamic Treatment Regimes

Description

Methods to estimate optimal dynamic treatment regimes using Bayesian likelihood-based regression approach as described in Yu, W., & Bondell, H. D. (2023) doi:10.1093/jrsssb/qkad016 Uses backward induction and dynamic programming theory for computing expected values. Offers options for future parallel computing.

Author(s)

Maintainer: Jeremy Lim jeremylim23@gmail.com

Authors:

References

Yu, W., & Bondell, H. D. (2023), “Bayesian Likelihood-Based Regression for Estimation of Optimal Dynamic Treatment Regimes”, Journal of the Royal Statistical Society Series B: Statistical Methodology, 85(3), 551-574. doi:10.1093/jrsssb/qkad016

See Also

generate_dataset() for generating a toy dataset to test the model fitting on

BayesLinRegDTR.model.fit() for obtaining an estimated posterior distribution of the optimal treatment option at a user-specified prediction stage

Useful links:

Main function for fitting a Bayesian likelihood-based linear regression model

Description

Fits the Bayesian likelihood-based linear model to obtain an estimated posterior distribution of the optimal treatment option at a user-specified prediction stage. Uses backward induction and dynamic programming theory for computing expected values.

Usage

BayesLinRegDTR.model.fit(
  Dat.train,
  Dat.pred,
  n.train,
  n.pred,
  num_stages,
  num_treats,
  p_list,
  t,
  R = 30,
  tau = 0.01,
  B = 10000,
  nu0 = 3,
  V0 = mapply(diag, p_list, SIMPLIFY = FALSE),
  alph = 1,
  gam = 1,
  showBar = TRUE
)

Arguments

Dat.train

Training data in format returned by generate_dataset: organised as a list of \{y, X_1, X_2..., X_{num\_stages}, A\} where y is a vector of the final outcomes, X_1, X_2..., X_{num\_stages} is a list of matrices of the intermediate covariates and A is an n.train \times num\_stages matrix of the assigned treatments, where num_stages is the total number of stages

Dat.pred

Prediction data in format returned by generate_dataset: organised as a list of \{X_1, X_2..., X_t, A\} where X_1, X_2..., X_t is a list of matrices of the intermediate covariates and A is an n.pred \times (t-1) matrix of the assigned treatments, where t is the prediction stage

n.train

Number of samples/individuals in the training data

n.pred

Number of samples/individuals in the prediction data

num_stages

Total number of stages

num_treats

Vector of number of treatment options at each stage

p_list

Vector of intermediate covariate dimensions for each stage

t

Prediction stage t, where t \leq num_stages

R

Draw size from distribution of intermediate covariates. default: 30

tau

Normal prior scale parameter for regression coefficients. Should be specified with a small value. default: 0.01

B

Number of MC draws from posterior of regression parameters. default 10000

nu0

Inverse-Wishart prior degrees of freedom for regression error Vcov matrix. Ignored if using a univariate dataset. default: 3

V0

List of Inverse-Wishart prior scale matrix for regression error Vcov matrix. Ignored if using a univariate dataset. default: list of identity matrices

alph

Inverse-Gamma prior shape parameter for regression error variance of y. default: 1

gam

Inverse-Gamma prior rate parameter for regression error variance of y. default: 1

showBar

Whether to show a progress bar. Uses API from progressr and future for parallel integration deafult: TRUE

Details

Utilises a future framework, so to enable parallel processing and register a parallel backend, plan and registerDoFuture must be called first.

Additionally, progress bars use progressr API, and a non-default progress bar (e.g. cli) is recommended. See below or registerDoFuture and handlers for examples.

Note that to have a progress bar for the parallel sections, future must be used. To turn off the immediate warnings, use options(BRDTR_warn_imm = FALSE).

Value

GCV_results

An array of dimension n.pred \times num\_treats[t] \times B, indicating the expected value under each treatment option at stage t.

post.prob

An n.pred \times num\_treats[t] matrix of the posterior probability that each treatment type at stage t is optimal

MC_draws.train

A list of Monte Carlo draws containing:

  • sigmat_B_list - A list of length num_stages with each element a vector of size B \times p\_list[t]

  • Wt_B_list - A list of length num_stages with each element a matrix of size B \times p\_list[t]

  • beta_B - A list of length B

  • sigmay_2B - A list of length B

Examples

# Code does not run within 10 seconds, so don't run

# -----------------------------
# 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)

Compute Monte Carlo Draws from Multivariate Dataset

Description

Obtain Monte Carlo draws from posterior distribution of stagewise regression parameters

Usage

compute_MC_draws_mvt(
  Data,
  tau,
  num_treats,
  B,
  nu0 = 3,
  V0 = mapply(diag, p_list, SIMPLIFY = FALSE),
  alph,
  gam,
  p_list,
  showBar = TRUE
)

Arguments

Data

Observed data organised as a list of \{y, X, A\} where y is a vector of the final outcomes, X is a list of matrices of the intermediate covariates and A is a matrix of the assigned treatments

tau

Prior precision scale. Should be specified with a small value

num_treats

Vector of number of treatment options at each stage

B

Number of MC draws

nu0

Inverse-Wishart degres of freedom. default: 3

V0

Inverse-Wishart scale matrix. default: diagonalisation of p_list

alph

Inverse-Gamma prior shape parameter for regression error variance of y. default: 1

gam

Inverse-Gamma prior rate parameter for regression error variance of y. default: 1

p_list

Vector of dimension for each stage

showBar

Whether to show a progress bar. Uses bar from progress_bar deafult: TRUE

Value

Monte Carlo draws??? A list containing:

  1. sigmat_B_list: Desc. A list of length num_stages with each element a vector of size B x p_t

  2. Wt_B_list: Desc. A list of length num_stages with each element a matrix of size B x p_t

  3. beta_B: Desc. A list of length B

  4. sigmay_2B: Desc. A list of length B

Compute Monte Carlo Draws from Univariate Dataset

Description

Obtain Monte Carlo draws from posterior distribution of stagewise regression parameters

Usage

compute_MC_draws_uvt(
  Data,
  tau,
  num_treats,
  B,
  alph,
  gam,
  p_list,
  showBar = TRUE
)

Arguments

Data

Observed data organised as a list of \{y, X, A\} where y is a vector of the final outcomes, X is a list of matrices of the intermediate covariates and A is a matrix of the assigned treatments

tau

Prior precision scale. Should be specified with a small value

num_treats

Vector of number of treatment options at each stage

B

Number of MC draws

alph

Inverse-Gamma prior shape parameter for regression error variance of y. default: 1

gam

Inverse-Gamma prior rate parameter for regression error variance of y. default: 1

p_list

Vector of dimension for each stage

showBar

Whether to show a progress bar. Uses bar from progress_bar deafult: TRUE

Value

Monte Carlo draws??? A list containing:

  1. thetat_B_list: Desc. A list of length num_stages with each element a vector of length B

  2. sigmat_2B_list: Desc. A list of length num_stages with each element a vector of length B

  3. beta_B: Desc. A list of length B

  4. sigmay_2B: Desc. A list of length B

Generate a toy dataset in the right format for testing BayesLinRegDTR.model.fit

Description

Generates a toy dataset simulating observed data of treatments over time with final outcomes and intermediate covariates. Follows the method outlined in Toy-Datagen on Github

Usage

generate_dataset(n, num_stages, p_list, num_treats)

Arguments

n

Number of samples/individuals to generate

num_stages

Total number of stages per individual

p_list

Vector of dimension for each stage

num_treats

Vector of number of treatment options at each stage

Value

Observed data organised as a list of \{y, X_1, X_2..., X_{num\_stages}, A\} where y is a vector of the final outcomes, X_1, X_2..., X_{num\_stages} is a list of matrices of the intermediate covariates and A is an n \times num\_stages matrix of the assigned treatments

Examples

# -----------------------------
# Initialise Inputs
# -----------------------------
n           <- 5000
num_stages  <- 3
p_list_uvt  <- rep(1, num_stages)
p_list_mvt  <- c(1, 3, 3)
num_treats  <- rep(3, num_stages)

# -----------------------------
# Main
# -----------------------------
Data_uvt    <- generate_dataset(n, num_stages, p_list_uvt, num_treats)
Data_mvt    <- generate_dataset(n, num_stages, p_list_mvt, num_treats)

Generate Multivariate dataset

Description

Generate Multivariate dataset

Usage

generate_dataset_mvt(n, num_stages, p_list, num_treats)

Arguments

n

Number of samples/individuals to generate

num_stages

Total number of stages per individual

p_list

Vector of dimension for each stage

num_treats

Vector of number of treatment options at each stage

Value

Observed data organised as a list of \{y, X, A\} where y is a vector of the final outcomes, X is a list of matrices of the intermediate covariates and A is a matrix of the assigned treatments

Generate Univariate Dataset

Description

Generate Univariate Dataset

Usage

generate_dataset_uvt(n, num_stages, num_treats)

Arguments

n

Number of samples/individuals to generate

num_stages

Total number of stages per individual

num_treats

Vector of number of treatment options at each stage

Value

Observed data organised as a list of \{y, X, A\} where y is a vector of the final outcomes, X is a list of matrices of the intermediate covariates and A is a matrix of the assigned treatments

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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