Application of Dynamic Linear Models to Random Allocation Clinical Trials with Covariates

TL;DR

This paper enhances Bayesian Adaptive Allocation Models by incorporating covariates into Dynamic Linear Models, reducing treatment allocation costs and time to identify preferred treatments in clinical trials.

Contribution

It introduces a covariate-inclusive DLM approach for Bayesian adaptive allocation, improving efficiency and providing sensitivity and power analyses.

Findings

Reduced treatment allocation budget and time to identify preferred treatment.

Sensitivity analysis on mean, variance, and covariate coefficients.

Power analysis using Bayes Factor to assess unallocated patient proportions.

Abstract

A recent method using Dynamic Linear Models to improve preferred treatment allocation budget in random allocation models was proposed by Lee, Boone, et al (2020). However this model failed to include the impact covariates such as smoking, gender, etc, had on model performance. The current paper addresses random allocation to treatments using the DLM in Bayesian Adaptive Allocation Models with a single covariate. We show a reduced treatment allocation budget along with a reduced time to locate preferred treatment. Furthermore, a sensitivity analysis is performed on mean and variance parameters and a power analysis is conducted using Bayes Factor. This power analysis is used to determine the proportion of unallocated patient budgets above a specified cutoff value. Additionally a sensitivity analysis is conducted on covariate coefficients.