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A flexible Bayesian framework for individualized inference via adaptive borrowing.

Ziyu Ji1, Julian Wolfson1

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St.SE, Minneapolis, MN 55455, USA.

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|January 13, 2022
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Summary
This summary is machine-generated.

A new data-driven multisource exchangeability model (dMEM) improves individualized health inferences by efficiently integrating data from numerous sources. This Bayesian approach enhances estimation precision for individual-level parameters using smartphone data.

Keywords:
Bayesian model averagingIndividualized inferenceMultisource data borrowingSupplementary data

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Area of Science:

  • Computational statistics
  • Biostatistics
  • Digital health

Background:

  • High-resolution health data capture technologies are increasing, driving demand for individual-level parameter inference.
  • Integrating multisource population data for improved individualized inference remains a challenge.
  • Existing multisource exchangeability models (MEM) face computational limitations with a large number of supplementary sources.

Purpose of the Study:

  • To propose a computationally tractable and data-efficient method for individualized inference using a large number of supplementary data sources.
  • To develop a novel approach, the data-driven MEM (dMEM), for enhanced parameter estimation.
  • To apply dMEM to real-world individual-level human behavior and mental well-being data.

Main Methods:

  • Developed a two-stage approach, dMEM, incorporating source selection and clustering.
  • Applied dMEM to analyze individual-level human behavior and mental well-being data from smartphones.
  • Compared dMEM performance against a standard no-borrowing method and other competing approaches.

Main Results:

  • dMEM significantly increases individual-level estimation precision by 84% compared to a no-borrowing method.
  • The proposed dMEM approach outperforms competing methods in 80% of individuals analyzed.
  • dMEM demonstrates computational tractability and data efficiency for incorporating numerous data sources.

Conclusions:

  • dMEM offers a scalable and effective solution for individualized inference in the era of big health data.
  • The method enhances the precision of individual-level parameter estimation in digital health applications.
  • dMEM represents a significant advancement in leveraging multisource data for personalized health insights.