The Differential Time-Varying Effect Model (DTVEM): A tool for diagnosing and modeling time lags in intensive longitudinal data
Open accessAbstract
With the recent growth in intensive longitudinal designs and the corresponding demand for methods to analyze such data, there has never been a more pressing need for user-friendly analytic tools that can identify and estimate optimal time lags in intensive longitudinal data. The available standard exploratory methods to identify optimal time lags within univariate and multivariate multiple-subject time series are greatly underpowered at the group (i.e., population) level. We describe a hybrid exploratory-confirmatory tool, referred to herein as the Differential Time-Varying Effect Model (DTVEM), which features a convenient user-accessible function to identify optimal time lags and estimate these lags within a state-space framework. Data from an empirical ecological momentary assessment study are then used to demonstrate the utility of the proposed tool in identifying the optimal time lag for studying the linkages between nervousness and heart rate in a group of undergraduate students. Using a simulation study, we illustrate the effectiveness of DTVEM in identifying optimal lag structures in multiple-subject time-series data with missingness, as well as its strengths and limitations as a hybrid exploratory-confirmatory approach, relative to other existing approaches.
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Dynamic models of heart rate captured in daily life can be effectively estimated even with very large proportions of missing data (e.g. >70% missingness for each subject) using full-information maximum likelihood estimation.
“Moreover, dynamic models of HR captured in daily life can be effectively estimated even with very large proportions of missing data (e.g. >70% missingness for each subject) using full-information maximum likelihood estimation60.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [60]
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- Authors
- Jacobson, N. C., Chow, S.-M., & Newman, M. G.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Jacobson, Nicholas C.
- Venue
- Behavior Research Methods
- Published
- 2018-08-17
- Volume
- 51
- Issue
- 1
- Pages
- 295-315
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- PMC
- PMC6395514
- OpenAlex
- W2886837415
- Cited by
- 63
- References
- 96
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 8 · 2023: 7 · 2022: 7 · 2021: 11 · 2020: 11 · 2019: 4 · 2018: 1
- FWCI
- 8.84
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Behavior Research Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- ScholarSphere (Penn State Libraries) (submittedVersion, cc-by)
- Funders
- National Institute on Alcohol Abuse and Alcoholism (U24AA027684)
- National Institute of Mental Health (R01MH115128)
- National Institute of General Medical Sciences (R01GM105004)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- intensive longitudinal data, state-space model, ecological momentary assessment, anxiety, heart rate, missing data
- MeSH terms
- Data Interpretation, Statistical, Humans, Longitudinal Studies, Time Factors, Models, Statistical
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