generate_dataset_mvt

internal

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.

Wei-Chang Yu

BayesRegDTR

Bayesian Regression for Dynamic Treatment Regimes

Jeremy Lim

Weichang Yu

generate_dataset_mvt function

<dl><dt>n</dt>
<dd>Number of samples/individuals to generate</dd>
<dt>num_stages</dt>
<dd>Total number of stages per individual</dd>
<dt>p_list</dt>
<dd>Vector of dimension for each stage</dd>
<dt>num_treats</dt>
<dd>Vector of number of treatment options at each stage</dd></dl>

Arguments

Generate Multivariate dataset — generate_dataset_mvt

<dl>

<dt>n</dt>
<dd>Number of samples/individuals to generate</dd>


<dt>num_stages</dt>
<dd>Total number of stages per individual</dd>


<dt>p_list</dt>
<dd>Vector of dimension for each stage</dd>


<dt>num_treats</dt>
<dd>Vector of number of treatment options at each stage</dd>

</dl>

generate_dataset_mvt: Generate Multivariate dataset

Description

Usage

Value

Arguments