title: A time-varying effect model for intensive longitudinal data.
abstract: Understanding temporal change in human behavior and psychological processes is a central issue in the behavioral sciences. With technological advances, intensive longitudinal data (ILD) are increasingly generated by studies of human behavior that repeatedly administer assessments over time. ILD offer unique opportunities to describe temporal behavioral changes in detail and identify related environmental and psychosocial antecedents and consequences. Traditional analytical approaches impose strong parametric assumptions about the nature of change in the relationship between time-varying covariates and outcomes of interest. This article introduces time-varying effect models (TVEMs) that explicitly model changes in the association between ILD covariates and ILD outcomes over time in a flexible manner. In this article, we describe unique research questions that the TVEM addresses, outline the model-estimation procedure, share a SAS macro for implementing the model, demonstrate model utility with a simulated example, and illustrate model applications in ILD collected as part of a smoking-cessation study to explore the relationship between smoking urges and self-efficacy during the course of the pre- and postcessation period.
authors: The Methodology Center, The Pennsylvania State University, 204 East Calder Way, Suite 400, State College, PA 16801, USA. xzt1@psu.edu
authors: Xianming
authors: X
authors: Tan
authors: Mariya P
authors: MP
authors: Shiyko
authors: Runze
authors: R
authors: Li
authors: Yuelin
authors: Y
authors: Li
authors: Lisa
authors: L
authors: Dierker
mesh_terms: D000339:Affect; D035841:Behavioral Research / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies / Q000706:statistics & numerical data*; D015233:Models, Statistical; D055361:Multilevel Analysis; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D020377:Self Efficacy; D016540:Smoking Cessation / Q000523:psychology / Q000706:statistics & numerical data; D018709:Statistics, Nonparametric; D013997:Time Factors