Socioeconomic Status and Offending

Reitzel, John David

doi:10.1002/9781118929803.ewac0476

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Crime patterns show similar characteristics to spatial mobile health data. They are affected by covariates such as temperature and precipitation [ 36 ] and can be linked to many other features such as census data of household incomes or education [ 37 ] (analogous to covariates of movement such as age, physical fitness, etc. [ 38 ]).…”

Section: Resultsmentioning

confidence: 99%

Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions

et al. 2021

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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, interpreting biomedical sensor data needs 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. This framework corrects for covariates to provide accurate assessments of the significance 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, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI 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 and also compare the performance to other models. Specifically, 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.

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Section: Resultsmentioning

confidence: 99%

Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions

et al. 2021

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Understanding Differential Development of Imbalance as Predictive of Risk for Behavioral Outcomes Among Justice-Involved Youth

Wojciechowski

2022

J Dev Life Course Criminology

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Bayesian Structural Time Series for Biomedical Sensor Data: A Flexible Modeling Framework for Evaluating Interventions

Liu

Spakowicz

Ash

et al. 2020

Preprint

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The development of mobile health technology has the potential to contribute greatly to personalized medicine. Wearable sensors can assist with determining the proper treatment plans for individuals, provide quantitative information to physicians, or give individuals an objective measurement of their health. However, though treatments and interventions have become more targeted and specific, measuring the causal impact of these actions require more careful considerations of complex covariate structures as well as temporal and spatial properties of the data. Thus, emerging data from sensors and wearables in the near future will make use of and require complex models. Here, we describe a general statistical framework for sensor and wearable data that applies a Bayesian structural time series model to analyze and understand various behavior and health data collected in different environments. We show the wide applicability of this modelling framework, and how it corrects for covariates and biases to provide accurate assessments of intervention. Furthermore, it allows for a time dependent confidence interval of impact through its use of Bayesian estimation. We give three main examples, physical sensor data, environmental air sensors and longitudinal behavioral data to show the effect of various interventions through parameter estimation and comparison in pre-and post-intervention periods. The Bayesian structural time series model shows robust performance in a wide variety of tasks, further supporting its applicability to current and future mobile health and sensor data types.

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Socioeconomic Status and Offending

Cited by 3 publications

References 4 publications

Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions

Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions

Understanding Differential Development of Imbalance as Predictive of Risk for Behavioral Outcomes Among Justice-Involved Youth

Bayesian Structural Time Series for Biomedical Sensor Data: A Flexible Modeling Framework for Evaluating Interventions

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