ArticlePsychological methods2015
Modeling intensive longitudinal data with mixtures of nonparametric trajectories and time-varying effects.
Article in Psychological methods, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Heterogeneity in perinatal depression: how far have we come? A systematic review.Archives of women's mental health · 2017Pooled it
- Mechanisms of behavior change during alcohol treatment among negative affect drinkers: A time-varying effect model analysis using 84 consecutive days of ecological momentary assessment.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2024Trial
- Time-varying moderation of treatment outcomes by illness duration and comorbid depression in generalized anxiety disorder.Journal of consulting and clinical psychology · 2019Trial
- Exploring the nature and relationship of tobacco cigarette craving and withdrawal.Journal of psychopathology and clinical science · 2026Article
- Tobacco withdrawal system recovery time assessed during smoking satiety predicts smoking behavior change among individuals who smoke cigarettes daily.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2026Article
- Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer's disease progression study.BioData mining · 2025Article
- Biclustering Multivariate Longitudinal Data with Application to Recovery Trajectories of White Matter After Sport-Related Concussion.Data science in science · 2024Article
- Time-varying effect modeling with intensive longitudinal data: Examining dynamic links among craving, affect, self-efficacy and substance use during addiction recovery.Addiction (Abingdon, England) · 2023Article
- Companion: A Pilot Randomized Clinical Trial to Test an Integrated Two-Way Communication and Near-Real-Time Sensing System for Detecting and Modifying Daily Inactivity among Adults >60 Years-Design and Protocol.Sensors (Basel, Switzerland) · 2023Article
- Time-Varying Associations Between Device-Based and Ecological Momentary Assessment-Reported Sedentary Behaviors and the Concurrent Affective States Among Adolescents: Proof-of-Concept Study.JMIR formative research · 2022Article
- Estimation and inference for the mediation effect in a time-varying mediation model.BMC medical research methodology · 2022Article
- Development of a mixture model allowing for smoothing functions of longitudinal trajectories.Statistical methods in medical research · 2021Article
- A BAYESIAN TIME-VARYING EFFECT MODEL FOR BEHAVIORAL MHEALTH DATA.The annals of applied statistics · 2020Article
- Intensive longitudinal methods for studying the role of self-regulation strategies in substance use behavior change.Current addiction reports · 2020Article
- Sensitivity and specificity of information criteria.Briefings in bioinformatics · 2020Review
- Trajectory of Smoking and Incidence of Atherosclerotic Cardiovascular Disease among Korean Young Adult Men.International journal of environmental research and public health · 2019Article
- Commentary: Pediatric Digital Health Supported by the National Institutes of Health.Journal of pediatric psychology · 2019Article
- Flexibly modeling alcohol use disorder risk: How many drinks should we count?Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2019Article
- Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists.Statistics surveys · 2019Article
- Mind-wandering rates fluctuate across the day: evidence from an experience-sampling study.Cognitive research: principles and implications · 2018Article
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Authors and funding
5 authors.
Funding
Abstract
Behavioral scientists increasingly collect intensive longitudinal data (ILD), in which phenomena are measured at high frequency and in real time. In many such studies, it is of interest to describe the pattern of change over time in important variables as well as the changing nature of the relationship between variables. Individuals' trajectories on variables of interest may be far from linear, and the predictive relationship between variables of interest and related covariates may also change over time in a nonlinear way. Time-varying effect models (TVEMs; see Tan, Shiyko, Li, Li, & Dierker, 2012) address these needs by allowing regression coefficients to be smooth, nonlinear functions of time rather than constants. However, it is possible that not only observed covariates but also unknown, latent variables may be related to the outcome. That is, regression coefficients may change over time and also vary for different kinds of individuals. Therefore, we describe a finite mixture version of TVEM for situations in which the population is heterogeneous and in which a single trajectory would conceal important, interindividual differences. This extended approach, MixTVEM, combines finite mixture modeling with non- or semiparametric regression modeling, to describe a complex pattern of change over time for distinct latent classes of individuals. The usefulness of the method is demonstrated in an empirical example from a smoking cessation study. We provide a versatile SAS macro and R function for fitting MixTVEMs.
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