PDA in Action: Ten Principles for High-Quality Multi-Site Clinical Evidence Generation

TL;DR

This paper establishes ten best-practice principles for conducting high-quality multi-site clinical research using Privacy-Preserving Distributed Algorithms, addressing challenges like heterogeneity and data sharing in distributed research networks.

Contribution

It provides a comprehensive, principled framework for employing PDA in multi-site studies to improve the quality, transparency, and reproducibility of real-world evidence generation.

Findings

Ten principles covering all research phases.

Framework enhances reliability and reproducibility.

Guides effective use of distributed learning algorithms.

Abstract

Background: Distributed Research Networks (DRNs) offer significant opportunities for collaborative multi-site research and have significantly advanced healthcare research based on clinical observational data. However, generating high-quality real-world evidence using fit-for-use data from multi-site studies faces important challenges, including biases associated with various types of heterogeneity within and across sites and data sharing difficulties. Over the last ten years, Privacy-Preserving Distributed Algorithms (PDA) have been developed and utilized in numerous national and international real-world studies spanning diverse domains, from comparative effectiveness research, target trial emulation, to healthcare delivery, policy evaluation, and system performance assessment. Despite these advances, there remains a lack of comprehensive and clear guiding principles for generating high-quality real-world evidence through collaborative studies leveraging the methods under PDA. Objective: The paper aims to establish ten principles of best practice for conducting high-quality multi-site studies using PDA. These principles cover all phases of research, including study preparation, protocol development, analysis, and final reporting. Discussion: The ten principles for conducting a PDA study outline a principled, efficient, and transparent framework for employing distributed learning algorithms within DRNs to generate reliable and reproducible real-world evidence.