Bayesian Integrated Learning of Longitudinal Dose-Response Relationships via Decentralized Clinical Trials

Authors

Jingyi Zhang
Tuo Wang
Yongming Qu
Fangrong Yan
Suyu Liu
Ruitao Lin

Language

English

Publication Date

12-1-2025

Journal

Statistics in Medicine

DOI

10.1002/sim.70338

PMID

41339943

PMCID

PMC12675892

PubMedCentral® Posted Date

12-3-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Decentralized clinical trials (DCTs) extend trial activities beyond traditional sites, enhancing access, convenience, efficiency, and result generalizability. They are particularly promising for chronic conditions like diabetes and obesity, which require longer study durations to evaluate drug effects. However, decentralized data collection raises concerns about increased variability and potential biases. This paper presents a novel Bayesian integrated learning procedure to analyze dose-response relationships using longitudinal data from a phase II DCT that combines centralized and decentralized data collection. We generalize a parametric exponential decay model to handle mixed data sources and apply Bayesian spike-and-slab priors to address biases and uncertainties from decentralized measurements. Our model enables data-adaptive integration of information from both centralized and decentralized sources. Through simulations and sensitivity analyses, we show that the proposed approach achieves favorable performance across various scenarios. Notably, the method matches the efficiency of traditional trials when decentralized data collection introduces no additional variability or error. Even when such issues arise, it remains less biased and more efficient than naïve methods that rely solely on centralized data or simply pool data from both sources.

Keywords

Bayes Theorem, Humans, Dose-Response Relationship, Drug, Longitudinal Studies, Computer Simulation, Clinical Trials, Phase II as Topic, Models, Statistical, Bias, data integration, decentralized clinical trial, dose‐response model, phase II trial, spike‐and‐slab prior

Published Open-Access

yes

Recommended Citation

Zhang, Jingyi; Wang, Tuo; Qu, Yongming; et al., "Bayesian Integrated Learning of Longitudinal Dose-Response Relationships via Decentralized Clinical Trials" (2025). Faculty, Staff and Student Publications. 6153.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6153