Video: Time-Varying Effect Modeling for Multiple Data Types

February 17, 2016:
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Methodology Center Scientific Director Stephanie Lanza recently presented a video lecture as part of the “Medicine: Mind the Gap” National Institutes of Health (NIH) seminar series. In the one-hour lecture, she discusses time-varying effect modeling (TVEM) and how it can be used to answer questions about the way relationships change over time. This lecture introduces TVEM within the context of two studies: a nicotine intervention that uses ecological momentary assessments (EMA) and an e-cigarette study that uses cross-sectional data that spans developmental age.

The “Medicine: Mind the Gap” lecture series explores issues at the intersection of research, evidence, and clinical practice—areas in which conventional wisdom may be contradicted by recent evidence. The goal of the series is to engage the NIH community in thought-provoking discussions about their role in helping to guide today’s research.
Watch the video

New Grant: Family Emotional Climate and Adolescent Problem Behavior

February 9, 2016:

Methodology Center Investigator Michael Russell and his Co-PI Dallas Swendeman of UCLA recently received a grant from the Jacobs Foundation to study fight1the relationship between family climate and problem behavior in adolescents. The investigators will collect ecological momentary assessments (EMA) and audio sensor data from cell phones while safeguarding participants’ privacy. They will measure the emotional climate in homes with adolescents by capturing voice characteristics such as pitch and volume. These measurements will be compared to self-reports of household conflict with the goal of creating in-the-moment interventions triggered by audio monitoring.

Michael is excited about the unique way this project will explore family interactions. “Traditionally, things like family conflict and expressed hostility can only be measured through interviews or questionnaires after the fact,” he said. “But the audio-sensing app will allow us to identify conflict, hostility, and supportive interactions as they occur in real time.” Michael added that this information could eventually lead to the development of brief, smartphone-based interventions that help families manage conflict in the moment.

The investigators will recruit 50 homes with adolescents for the study, with half of the adolescents having a history of substance use. Participants will complete EMA four times per day for two weeks, and audio of family interactions will be monitored continuously during this time. The investigators will use time-varying effect modeling and multilevel modeling to examine the relationship between emotional climate and youth behavior.

Featured Article: Nicotine Dependence Diminishes the Effect of Smoking on Mood

November 11, 2015:
women smokerResearch has shown that some adolescents experience nicotine dependence at low levels of smoking (DiFranza et al., 2000; O’Loughlin et al., 2003). Other results indicate that early nicotine dependence strongly predicts future smoking (Dierker & Mermelstein, 2010; DiFranza et al., 2002). A recent paper in the journal Addictive Behavior, “Nicotine-dependence-varying effects of smoking events on momentary mood changes among adolescents,” provides insight into the mechanisms that encourage and maintain nicotine dependence. In this paper, the authors apply time-varying effect modeling (TVEM) and other methods to examine the association between nicotine dependence and the impact of smoking on mood. Authors include Methodology Center investigators, affiliates, and collaborators Arielle Selya, Nicole Updegrove, Jennifer Rose, Lisa Dierker, Xianming Tan, Donald Hedeker, Runze Li, and Robin Mermelstein.

The authors recruited a subsample from the Social and Emotional Contexts of Adolescent Smoking Patterns Study who were classified as former experimental smokers, current experimental smokers, or regular smokers. Participants were followed for 24 months using several waves of questionnaires and week-long ecological momentary assessments (EMA). Some of their findings validated current theory: adolescents with low nicotine dependence experienced improved mood when smoking, but this same improvement in mood was not observed in adolescents with higher levels of nicotine dependence. In other words, smoking more only improved mood among individuals with low levels of nicotine dependence.

Interestingly, the findings did not support other aspects of current theories, which postulate that nicotine withdrawal generates negative reinforcement that is responsible for maintaining dependence (Tiffany et al., 2004). In the current study the correlation between negative affect and amount smoked was NOT significant at higher levels of nicotine dependence.

Lead author Arielle Selya, assistant professor of family and community medicine at The University of North Dakota, is excited about the prospect of untangling the factors that lead to and maintain nicotine dependence. “These findings support theories that positive reinforcement is important at early stages in the addiction process, but we were surprised to see no support for the role of negative reinforcement in maintaining more severe levels of addiction. Perhaps smokers are pre-emptively smoking before they experience withdrawal symptoms. Perhaps social and situational factors are stronger drivers of the average individual smoking event than are nicotine dependence symptoms. Future research can tease apart these different factors and help us to better understand why smokers keep smoking.

Open the article. (Journal access required)

References

Selya, A. S., Updegrove, N., Rose, J. S., Dierker, L., Tan, X., Hedeker, D., … & Mermelstein, R. J. (2015). Nicotine-dependence-varying effects of smoking events on momentary mood changes among adolescents. Addictive Behaviors, 41, 65-71. doi: 10.1016/j.addbeh.2014.09.028

Dierker, L., & Mermelstein, R. J. (2010). Early emerging nicotine-dependence symptoms: A signal of propensity for chronic smoking behavior in adolescents. The Journal of Pediatrics, 156(5), 818–822. doi: 10.1016/j.jpeds.2009.11.044

DiFranza, J. R., Rigotti, N. A., McNeill, A.D., Ockene, J. K., Savageau, J. A., St Cyr, D., & Coleman, M. (2000). Initial symptoms of nicotine dependence in adolescents.Tobacco Control, 9(3), 313–319. doi: 10.1136/tc.9.3.313

DiFranza, J. R., Savageau, J. A., Rigotti, N. A., Fletcher, K., Ockene, J. K., McNeill, A.D., … & Wood, C. (2002). Development of symptoms of tobacco dependence in youths: 30 month follow up data from the DANDY study. Tobacco Control, 11(3), 228–235. doi: 10.1136/tc.11.3.228

O’Loughlin, J., DiFranza, J., Tyndale, R. F., Meshefedjian, G., McMillan-Davey, E., Clarke, P. B., … & Paradis, G. (2003). Nicotine-dependence symptoms are associated with smoking frequency in adolescents. American Journal of Preventive Medicine, 25(3), 219–225. doi: 10.1016/S0749379703001983

Tiffany, S. T., Conklin, C. A., Shiffman, S., & Clayton, R. R. (2004). What can dependence theories tell us about assessing the emergence of tobacco dependence?Addiction, 99(Suppl. 1), 78–86. doi: 10.1111/j.1360-0443.2004.00734.x

Major Revision of SAS TVEM Macro for Intensive Longitudinal Data

October 14, 2015:image of sample TVEM plot

The latest version of the TVEM (time-varying effect modeling) SAS macro (v. 3.1.0) offers several improvements over the previous version (v 2.1.1). Three macros from the previous suite have been consolidated into a single macro with simplified syntax for ease of use. Also, the new macro has the ability to model within-subject correlation using random effects or a robust sandwich variance estimator. Other improvements have been made to the onscreen output, the ability to generate output datasets, and the ability to generate plots in different ways.

TVEM allows researchers to answer new questions using intensive longitudinal data and mature panel studies, as well as answer questions about age-varying effects using less intensive data.

Traditional analytic methods assume that covariates have constant effects on a time-varying outcome. The TVEM SAS macro allows the effects of covariates to vary with time. The macro enables researchers to answer new research questions about how relationships change over time. The new version of the macro does not currently include accommodate zero-inflated Poisson (ZIP) outcomes. The previous version (2.1.1) of the %TVEM ZIP macro is still available.

Download the macro or read more.

Featured Article: Which Young Smokers Will End Up Addicted?

October 12, 2015:
Stephanie LanzaSV

Early milestones in the development of smoking, such as first cigarette, experimental smoking, and onset of regular smoking, are key risk factors for later nicotine dependence (Dierker et al., 2008). The risk associated with age of smoking initiation has been studied widely, but less research has examined the link between the age of onset of regular smoking and later dependence. In a new article to appear in Addictive Behaviors, Methodology Center Investigators Stephanie Lanza and Sara Vasilenko apply time varying effect modeling (TVEM) to explore the link between age of onset of regular smoking and adult nicotine dependence.

This brief article is the first to apply TVEM to explore the complex association between the age of onset of a risky behavior and later diagnosis.

Using a sample of 15,748 adults from the National Epidemiologic Survey on Alcohol and Related Conditions, the authors applied TVEM to model nicotine dependence in adulthood as a flexible function of age of first regular smoking. They found that adult nicotine dependence is highest for people who began to smoke regularly around age 10. The risk for adult dependence decreased steadily to about half for people who initiated at age 18. The association between age of onset of regular smoking and adult dependence was stronger for females. People who initiated regular smoking in adulthood had lower rates of nicotine dependence than those who initiated at any time during adolescence.

Open the article. (Journal access required)

References

Lanza, S. T., & Vasilenko, S. A. (2015). New methods shed light on age of onset as a risk factor for nicotine dependence. Addictive Behaviors50, 161-164.

Dierker L., He J., Kalaydjian A., Swendsen J., Degenhardt L., Glantz, M., et al. (2008). The importance of timing of transitions for risk of regular smoking and nicotine dependence. Annals of Behavioral Medicine, 36, 87-92.

Podcast: Getting Started with TVEM

September 15, 2015:

This podcast will introduce interested scientists to time-varying effect modeling (TVEM). Host Aaron Wagner talks with Methodology Center stl_savInvestigators Stephanie Lanza and Sara Vasilenko about the new types of questions scientists can answer by applying TVEM to existing data or to new studies.
Sara and Stephanie have been at the forefront of both applying TVEM and training scientists to use TVEM. Multiple participants from their TVEM workshop in June already have submitted TVEM manuscripts to journals. In this 25-minute podcast, they provide the introduction needed to determine whether TVEM could be useful in your work.

Podcast Timeline:

00:00 – Introduction

00:52 – Defining TVEM

02:07 – Time-varying versus time-invariant effects

04:00 – Questions TVEM can answer

07:08 – Why a “Year of TVEM?”

09:56 – Data and TVEM

11:00 – TVEM and ecological momentary assessments

13:03 – TVEM and panel data

15:41 – When not to use TVEM

20:12 – Getting started

23:32 – TVEM SAS macro