Bayesian Variable Selection for Multistate Markov Models with Interval-censored Data in an Ecological Momentary Assessment Study of Smoking Cessation

Koslovsky, Matthew D.; Swartz, Michael D.; Chan, Wenyaw; Leon‐Novelo, Luis; Wilkinson, Anna V.; Kendzor, Darla E.; Businelle, Michael S.

doi:10.1111/biom.12792

Cited by 17 publications

(21 citation statements)

References 51 publications

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“…While some studies demonstrating the success of generalized linear models for mobile health data do exist [24,25,[40][41][42], there is a lack of emphasis on considerations for the temporal aspect of interventions and the effect-size of interventions at each time point in the postintervention period. Specifically, generalized linear modeling frameworks lack the flexibility to evaluate the intervention's impact cumulatively in the post-intervention period.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…We additionally showcase how a rich dataset with complex covariates can benefit from the framework. While some studies demonstrating the success that linear models have on understanding such data do exist (18,19,(32)(33)(34), there is a lack of emphasis on considerations for the temporal aspect of interventions and how effective interventions are at different time points in the post-intervention period. Linear modeling frameworks lack the flexibility to evaluate the intervention's effect strength at all points in the post-intervention period, while existing Bayesian frameworks forecast based upon predictors (e.g., forecast continuous blood glucose based upon covariates like the ones we obtained from insulin and smartwatch devices) but do not evaluate deviation from this forecast following an intervention of known timing and duration to determine its impact (35)(36)(37).…”

Section: Discussionmentioning

confidence: 99%

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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“…An example of a ML algorithm that was developed to understand smokers’ behaviour and the urge to smoke as it changes during the quitting period was described by Koslovsky et al [ 67 ]. The authors proposed the use of Bayesian structural time series as a robust method to analyse the risk factors associated with different stages of the quitting process.…”

Section: Machine Learning Methods For Auto Intervention: the Future Of Smoking Cessation Appsmentioning

confidence: 99%

Are Machine Learning Methods the Future for Smoking Cessation Apps?

Abo-Tabik

Benn

Costen

2021

Sensors

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Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting of environmental conditions (e.g., location), craving status and/or smoking events as a method of data collection. Lastly, we propose that making use of more advanced machine learning methods while enabling the processing of information about the user’s circumstances in real time is likely to result in dramatic improvement in our understanding of smoking behaviour, while also increasing the effectiveness and ease-of-use of smoking cessation apps, by enabling the provision of timely, targeted and personalised intervention.

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Bayesian Variable Selection for Multistate Markov Models with Interval-censored Data in an Ecological Momentary Assessment Study of Smoking Cessation

Cited by 17 publications

References 51 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

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

Are Machine Learning Methods the Future for Smoking Cessation Apps?

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