Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates

Li, Yanling; Oravecz, Zita; Zhou, Shuai; Bodovski, Yosef; Barnett, Ian; Chi, Guangqing; Zhou, Yuan; Friedman, Naomi P.; Vrieze, Scott; Chow, Sy-Miin

doi:10.1007/s11336-021-09831-9

Cited by 5 publications

(1 citation statement)

References 60 publications

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“…More generally, while there is a consensus that models for ILD should accommodate individual-specific parameterizations (e.g., via random effects), there are open questions as to the conditions under which prediction will be more accurate using individual-specific versus aggregate model properties (e.g., the predicted random effects or the fixed effects). The comparison of the forecasting properties of aggregate and individual-specific model specifications, such as those provided by Nestler and Hamburg (2021) as well as Li et al (2022), begins to address this question.…”

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confidence: 99%

Guest Editors’ Introduction to the Special Issue on Forecasting with Intensive Longitudinal Data

2022

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The collection of longitudinal data in the social and behavioral sciences has been revolutionized by the widespread availability of information technologies such as smartphones, wearable technology, social media, digital learning, online games, and the Internet more generally. We use the term intensive longitudinal data (ILD) inclusively, to refer to the types of data available from such sources. ILD can arise from a broad range of data collection methods and research designs, and typically result in multivariate observations collected from multiple respondents over a relatively large number of time points. Examples of ILD from this collection of papers include experience sampling of alcohol and substance use, daily diaries of emotional states, students' interactions with a web-based math tutoring app, and university attendance records.ILD are typically collected on an ongoing basis over an extended duration of time, and in some applications data collection may continue indefinitely. This introduces the prospect of analyzing and acting upon ILD as they arrive, rather than waiting for data collection to be completed. This "online" approach to data analysis is common in domains such as engineering and machine learning, where it is usual for data to arrive on an ongoing basis. However, its potential application in the social and behavioral sciences remains largely unexplored. An initial step in this direction is to address the problem of forecasting with ILD, which is the focus of this special collection.The statistical analysis of ILD has motivated the development of novel modeling approaches. Examples from previous literature that are considered in this collection include multilevel/randomeffects extensions of vector auto-regression (VAR), dynamical structural equation modeling (SEM), and (non-) linear dynamical systems models. Hunter et al. ( 2022) provide an overview (and accompanying software) for filtering and forecasting techniques used in dynamical systems. Chow et al. ( 2022) consider how dynamical systems can be combined with control theory to "steer" a system towards a desired state, and consider the implications of this approach for personalized education. Lafit et al. (2021) provide data-driven insights into several factors that affect the predictive accuracy of multilevel VAR. This collection of papers also introduces some new modeling approaches. Li et al. (2022) introduce a multilevel zero-inflated Poisson (ZIP) model in which both the Poisson counts and the ZIP regimes have person-specific auto-regressive time dependency. Fisher et al. ( 2022) introduce an alternative to the random-effects approach, instead using regularization (adaptive LASSO) to extend VAR to multiple subjects while ensuring sparsity of the resulting solution.

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confidence: 99%

Guest Editors’ Introduction to the Special Issue on Forecasting with Intensive Longitudinal Data

2022

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Whole person recovery from substance use disorder: a call for research examining a dynamic behavioral ecological model of contexts supportive of recovery

Witkiewitz,

Tucker

2024

Addiction Research & Theory

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The ILHBN: challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols

Chow

Nahum‐Shani

Baker

et al. 2022

Translational Behavioral Medicine

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The ILHBN is funded by the National Institutes of Health to collaboratively study the interactive dynamics of behavior, health, and the environment using Intensive Longitudinal Data (ILD) to (a) understand and intervene on behavior and health and (b) develop new analytic methods to innovate behavioral theories and interventions. The heterogenous study designs, populations, and measurement protocols adopted by the seven studies within the ILHBN created practical challenges, but also unprecedented opportunities to capitalize on data harmonization to provide comparable views of data from different studies, enhance the quality and utility of expensive and hard-won ILD, and amplify scientific yield. The purpose of this article is to provide a brief report of the challenges, opportunities, and solutions from some of the ILHBN’s cross-study data harmonization efforts. We review the process through which harmonization challenges and opportunities motivated the development of tools and collection of metadata within the ILHBN. A variety of strategies have been adopted within the ILHBN to facilitate harmonization of ecological momentary assessment, location, accelerometer, and participant engagement data while preserving theory-driven heterogeneity and data privacy considerations. Several tools have been developed by the ILHBN to resolve challenges in integrating ILD across multiple data streams and time scales both within and across studies. Harmonization of distinct longitudinal measures, measurement tools, and sampling rates across studies is challenging, but also opens up new opportunities to address cross-cutting scientific themes of interest.

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Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates

Cited by 5 publications

References 60 publications

Guest Editors’ Introduction to the Special Issue on Forecasting with Intensive Longitudinal Data

Guest Editors’ Introduction to the Special Issue on Forecasting with Intensive Longitudinal Data

Whole person recovery from substance use disorder: a call for research examining a dynamic behavioral ecological model of contexts supportive of recovery

The ILHBN: challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols

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