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.
翻译:高分辨率数据采集技术在健康方面的爆炸性高清晰度数据采集技术的爆炸,使人们更加有兴趣对个人参数作出推断。虽然技术可以就单个个人提供大量数据,但如何最好地使用多源人口数据改进个化推论仍然是一个开放式研究问题。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分析一个原始来源的方法。MEM最初的开发是为了改进单一研究的推断,从一系列类似的先前研究中以非对称方式借用信息的单一研究,并进一步发展了在篮子试验中应用更加计算密集的对称借款;然而,即使用于非对称借款,如何最好地使用多源人口数据来改进个个性化人口数据分析数据数据数据数据,因此,计算负担的计算负担随着补充来源的数量而急剧增加,使得它不适合应用上百或上千个补充来源(即个人)的补充来源(即个人)可以帮助判断某个特定来源的分析。在本文件中,我们建议采用数据驱动MEM(dMEM)的两阶段方法,其中包括选择和组合,以便能够任意性地将一些来源用于在计算方法中,从而推算出一种可计算性地计算个人数据的计算方法。