Application of Bayesian Dynamic Linear Models to Random Allocation Clinical Trials

TL;DR

This paper introduces a Bayesian Dynamic Linear Model approach for clinical trial patient allocation, aiming to improve speed and efficiency over existing methods while reducing bias and ethical concerns.

Contribution

It proposes a novel DLM-based method for patient allocation in clinical trials, enhancing speed and reducing sample requirements compared to previous Bayesian approaches.

Findings

Faster patient allocation with fewer samples needed.

Effective identification of superior treatments.

Sensitivity analysis informs parameter choices.

Abstract

Random allocation models used in clinical trials aid researchers in determining which of a particular treatment provides the best results by reducing bias between groups. Often however, this determination leaves researchers battling ethical issues of providing patients with unfavorable treatments. Many methods such as Play the Winner and Randomized Play the Winner Rule have historically been utilized to determine patient allocation, however, these methods are prone to the increased assignment of unfavorable treatments. Recently a new Bayesian Method using Decreasingly Informative Priors has been proposed by \citep{sabo2014adaptive}, and later \citep{donahue2020allocation}. Yet this method can be time consuming if MCMC methods are required. We propose the use of a new method which uses Dynamic Linear Model (DLM) \citep{harrison1999bayesian} to increase allocation speed while also decreasing patient allocation samples necessary to identify the more favorable treatment. Furthermore, a sensitivity analysis is conducted on multiple parameters. Finally, a Bayes Factor is calculated to determine the proportion of unused patient budget remaining at a specified cut off and this will be used to determine decisive evidence in favor of the better treatment.