A flexible Bayesian framework for individualized inference via adaptive borrowing

TL;DR

This paper introduces a data-driven Bayesian framework called dMEM that efficiently integrates multiple data sources to improve individual-level inference, demonstrated on smartphone-based mental health data with significant accuracy gains.

Contribution

The paper presents dMEM, a novel two-stage Bayesian method that enables scalable, adaptive borrowing from numerous sources for personalized inference.

Findings

Increased individual estimation precision by 84%

Outperforms competing methods in 80% of cases

Effective application to smartphone-based mental health data

Abstract

The explosion in high-resolution data capture technologies in health has increased interest in making inferences about individual-level parameters. While technology may provide substantial data on a single individual, how best to use multisource population data to improve individualized inference remains an open research question. One possible approach, the multisource exchangeability model (MEM), is a Bayesian method for integrating data from supplementary sources into the analysis of a primary source. MEM was originally developed to improve inference for a single study by asymmetrically borrowing information from a set of similar previous studies and was further developed to apply a more computationally intensive symmetric borrowing in the context of basket trial; however, even for asymmetric borrowing, its computational burden grows exponentially with the number of supplementary sources, making it unsuitable for applications where hundreds or thousands of supplementary sources (i.e., individuals) could contribute to inference on a given individual. In this paper, we propose the data-driven MEM (dMEM), a two-stage approach that includes both source selection and clustering to enable the inclusion of an arbitrary number of sources to contribute to individualized inference in a computationally tractable and data-efficient way. We illustrate the application of dMEM to individual-level human behavior and mental well-being data collected via smartphones, where our approach increases individual-level estimation precision by 84% compared with a standard no-borrowing method and outperforms recently-proposed competing methods in 80% of individuals.