PT  - JOURNAL ARTICLE
AU  - Jason Liu
AU  - Daniel J. Spakowicz
AU  - Garrett I. Ash
AU  - Rebecca Hoyd
AU  - Andrew Zhang
AU  - Shaoke Lou
AU  - Donghoon Lee
AU  - Jing Zhang
AU  - Carolyn Presley
AU  - Ann Greene
AU  - Matthew Stults-Kolehmainen
AU  - Laura Nally
AU  - Julien S. Baker
AU  - Lisa M. Fucito
AU  - Stuart A. Weinzimer
AU  - Andrew V Papachristos
AU  - Mark Gerstein
TI  - Bayesian Structural Time Series for Biomedical Sensor Data: A Flexible Modeling Framework for Evaluating Interventions
AID  - 10.1101/2020.03.02.973677
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.03.02.973677
4099  - http://biorxiv.org/content/early/2020/08/18/2020.03.02.973677.short
4100  - http://biorxiv.org/content/early/2020/08/18/2020.03.02.973677.full
AB  - The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g. wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures as well as knowledge of the temporal and spatial properties of the data. Thus, biomedical sensor data need to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. We further show how this framework corrects for covariates to provide accurate assessments of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool around a specific Google implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the resulting software implementation to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity. In particular, we show how the framework is able to evaluate an exercise intervention’s effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.Competing Interest StatementThe authors have declared no competing interest.