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% compared with a standard no-borrowing method and outperforms recently-proposed competing methods in 80% of individuals.

翻译：高分辨率数据采集技术在健康方面的爆炸使得人们更加有兴趣对个人参数作出推断。虽然技术可以提供关于单个个人的实质性数据,但如何最好地使用多源人口数据来改进个化推断仍然是一个开放的研究问题。一种可能的办法,即多源互换模式(MEM),是巴伊西亚将补充来源的数据纳入对原始来源的分析的一种方法。MEM最初开发的目的是通过借用先前类似研究的信息,改进单一研究的推断;然而,其计算负担随着补充来源的数量的增多而成倍增加,使得它不适合适用于数百或数千个补充来源(即个人)能够帮助推断某个特定个人的应用。在本文件中,我们提出了数据驱动MEM(dEM)的两阶段办法,其中包括选择来源和组合,以便纳入任意数量的来源,从而以可计算和数据高效的方式帮助个人进行个化的推断。我们用智能手机方法对个人层面的行为和心理健康数据(即个人)进行应用,而最近通过智能手机精确度方法收集到的80 %的精确度数据。