Evidence map›Paper›PMID 27736149›Full record

ArticlePsychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors2016

Time-varying effect modeling to address new questions in behavioral research: Examples in marijuana use.

Stephanie T Lanza, Sara A Vasilenko, Michael A Russell

Abstract read
In one paragraph

Article in Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

43 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Stephanie T LanzaThe Methodology Center.
Sara A VasilenkoThe Methodology Center.
Michael A RussellThe Methodology Center.

Funding

THE REFINEMENT &APPLICATION OF MEDICAL DECISION MAKING IN PREVENTION SCIENCESP50DA010075 · NIDA · PENNSYLVANIA STATE UNIVERSITY-UNIV PARK · PI MURPHY, SUSAN A · 1996 to 2014
$24.5M
Pilot, Mentoring, and Professional Development CoreP50DA039838 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI COLLINS, LINDA M · 2015 to 2019
$13.9M
PREVENTION AND METHODOLOGY TRAINING (PAMT)T32DA017629 · NIDA · PENNSYLVANIA STATE UNIVERSITY-UNIV PARK · PI Eric D Claus, Rina D Eiden · 2005 to 2026
$9.5M
Dynamic Links Between Risk Factors, Substance Use, and Consequences: Ages 18-35R01DA037902 · NIDA · UNIVERSITY OF MINNESOTA · PI PATRICK, MEGAN E. · 2015 to 2018
$1.5M
Age-Varying Effects in the Epidemiology of Drug AbuseR01DA039854 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI LANZA, STEPHANIE T · 2015 to 2017
$676k
Advancing Tobacco Research by Integrating Systems Science and Mixture ModelsR01CA168676 · NCI · PENNSYLVANIA STATE UNIVERSITY, THE · PI LANZA, STEPHANIE T · 2012 to 2014
$498k
NCI NIH HHS R01 CA168676NIDA NIH HHS P50 DA010075NIDA NIH HHS P50 DA039838NIDA NIH HHS R01 DA037902NIDA NIH HHS R01 DA039854NIDA NIH HHS T32 DA017629
6 · The paper itself

Abstract

Time-varying effect modeling (TVEM), a statistical approach that enables researchers to estimate dynamic associations between variables across time, holds enormous potential to advance behavioral research. TVEM can address innovative questions about processes that unfold across different levels of time. We present a conceptual introduction to the approach and demonstrate 4 innovative ways to approach time in TVEM to advance research on the etiology of marijuana use. First, we examine changes in associations across historical time to understand how the link between marijuana use attitudes and marijuana use behavior has shifted from 1976 to present; gender differences in the relevance of attitudes diminished over time and were no longer significant after 2004. Second, we examine age-varying associations between heavy episodic drinking and marijuana use across developmental time and demonstrate that this dynamic association is substantially stronger during ages 14 to 16 compared with later ages. Third, we explore the complex association between age of onset of marijuana use and adult marijuana use to identify precise age ranges during which the onset of use is most risky, and demonstrate how this complex association is more salient for males. Finally, we examine changes in marijuana use as a function of time relative to the birth of first child and show how this transition is more crucial for females. All empirical examples in this methodological demonstration rely on existing data from cross-sectional or panel studies. We conclude with thoughts on future directions for the application and further development of TVEM in behavioral research. (PsycINFO Database Record

Indexed as

Models, PsychologicalSex CharacteristicsAdolescentAdultBehavioral ResearchCross-Sectional StudiesFemaleHumansLongitudinal StudiesMaleMarijuana AbuseMarijuana SmokingRisk

Identifiers

PMID27736149
PMCPMC5222706

What OpenQuestion holds

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Registered trials

None linked

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.