A flexible Bayesian framework for individualized inference via dynamic borrowing

by   Ziyu Ji, et al.

The explosion in high-resolution data capture technologies in health has increased interest in making inference 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 borrowing information from similar previous studies; however, 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 no-borrowing method and outperforms recently-proposed competing methods in 80 of individuals.



There are no comments yet.


page 29


Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model

Social dynamics is concerned primarily with interactions among individua...

Use of Historical Individual Patient Data in Analysis of Clinical Trials

Historical data from previous clinical trials, observational studies and...

Bayesian inference in hierarchical models by combining independent posteriors

Hierarchical models are versatile tools for joint modeling of data sets ...

Joint integrative analysis of multiple data sources with correlated vector outcomes

We propose a distributed quadratic inference function framework to joint...

Individualized Group Learning

Many massive data are assembled through collections of information of a ...

Nonparametric fusion learning: synthesize inferences from diverse sources using depth confidence distribution

Fusion learning refers to synthesizing inferences from multiple sources ...

Bayesian hierarchical modeling and analysis for physical activity trajectories using actigraph data

Rapid developments in streaming data technologies are continuing to gene...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.