ArticleMultivariate behavioral research
On the Use of Mixed Markov Models for Intensive Longitudinal Data.
Article in Multivariate behavioral research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- SMART-MC: Characterizing the Dynamics of Multiple Sclerosis Therapy Transitions Using a Covariate-Based Markov Model.Journal of the American Statistical Association · 2026Article
- Prevalence, predictors, correlates, and dynamic changes in the NIAAA-defined "recovery" definition.Alcohol, clinical & experimental research · 2025Article
- Characterising symptomatic substates in individuals on the psychosis continuum: a hidden Markov modelling approach.Psychological medicine · 2025Article
- Network analysis: An overview for mental health research.International journal of methods in psychiatric research · 2024Review
- Exposure-response modeling for nausea incidence for cotadutide using a Markov modeling approach.CPT: pharmacometrics & systems pharmacology · 2024Article
- The Google Health Digital Well-Being Study: Protocol for a Digital Device Use and Well-Being Study.JMIR research protocols · 2024Article
- Better, for worse, or both? Testing environmental sensitivity models with parenting at the level of individual families.Development and psychopathology · 2024Article
- Clustering Sequence Data with Mixture Markov Chains with Covariates Using Multiple Simplex Constrained Optimization Routine (MSiCOR).Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Capturing crisis dynamics: a novel personalized approach using multilevel hidden Markov modeling.Frontiers in psychiatry · 2024Article
- Utilizing biologic disease-modifying anti-rheumatic treatment sequences to subphenotype rheumatoid arthritis.Arthritis research & therapy · 2023Article
- Mapping the timescale of suicidal thinking.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A Comparison of Mathematical and Statistical Modeling with Longitudinal Data: An Application to Ecological Momentary Assessment of Behavior Change in Individuals with Alcohol Use Disorder.Bulletin of mathematical biology · 2022Article
- Bayesian estimation and model selection in group-based trajectory models.Psychological methods · 2022Article
- A BAYESIAN TIME-VARYING EFFECT MODEL FOR BEHAVIORAL MHEALTH DATA.The annals of applied statistics · 2020Article
- A Dynamic Framework for Modelling Set-Shifting Performances.Behavioral sciences (Basel, Switzerland) · 2019Article
- Autoregressive Generalized Linear Mixed Effect Models with Crossed Random Effects: An Application to Intensive Binary Time Series Eye-Tracking Data.Psychometrika · 2018Article
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Authors and funding
6 authors.
Funding
Abstract
Markov modeling presents an attractive analytical framework for researchers who are interested in state-switching processes occurring within a person, dyad, family, group, or other system over time. Markov modeling is flexible and can be used with various types of data to study observed or latent state-switching processes, and can include subject-specific random effects to account for heterogeneity. We focus on the application of mixed Markov models to intensive longitudinal data sets in psychology, which are becoming ever more common and provide a rich description of each subject's process. We examine how specifications of a Markov model change when continuous random effect distributions are included, and how mixed Markov models can be used in the intensive longitudinal research context. Advantages of Bayesian estimation are discussed and the approach is illustrated by two empirical applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.