Iterated multisource exchangeability models for individualized inference with an application to mobile sensor data

Roland Brown, Yingling Fan, Kirti Das, Julian Wolfson

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Researchers are increasingly interested in using sensor technology to collect accurate activity information and make individualized inference about treatments, exposures, and policies. How to optimally combine population data with data from an individual remains an open question. Multisource exchangeability models (MEMs) are a Bayesian approach for increasing precision by combining potentially heterogeneous supplemental data sources into analysis of a primary source. MEMs are a potentially powerful tool for individualized inference but can integrate only a few sources; their model space grows exponentially, making them intractable for high-dimensional applications. We propose iterated MEMs (iMEMs), which identify a subset of the most exchangeable sources prior to fitting a MEM model. iMEM complexity scales linearly with the number of sources, and iMEMs greatly increase precision while maintaining desirable asymptotic and small sample properties. We apply iMEMs to individual-level behavior and emotion data from a smartphone app and show that they achieve individualized inference with up to 99% efficiency gain relative to standard analyses that do not borrow information.

Original languageEnglish (US)
Pages (from-to)401-412
Number of pages12
JournalBiometrics
Volume77
Issue number2
Early online dateMay 15 2020
DOIs
StatePublished - Jun 2021

Bibliographical note

Publisher Copyright:
© 2020 The International Biometric Society

Keywords

  • heterogeneous data sources
  • individual-level inference
  • sensor technology
  • supplementary data

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