Evidence map›Paper›PMID 25209555›Full record

ArticleStatistics in medicine2014

Time-varying effect models for ordinal responses with applications in substance abuse research.

John J Dziak, Runze Li, Marc A Zimmerman, Anne Buu

Open access · greenAbstract read
In one paragraph

Article in Statistics in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.7field-weighted citation impact, top 9% of its field
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

13 citing papers in PubMed, 20 citations in OpenAlex.

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  10. Time-varying effect modeling to address new questions in behavioral research: Examples in marijuana use.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2016
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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

4 authors at 3 institutions in 1 country.

John J DziakThe Methodology Center, Pennsylvania State University, University Park, PA, 16802, U.S.A.
Runze Li
Marc A Zimmerman
Anne Buu
Pennsylvania State University · USIndiana University Bloomington · USUniversity of Michigan · US

Funding

THE REFINEMENT &APPLICATION OF MEDICAL DECISION MAKING IN PREVENTION SCIENCESP50DA010075 · NIDA · PENNSYLVANIA STATE UNIVERSITY-UNIV PARK · PI LANZA, STEPHANIE T · 1996 to 2014
$24.5M
Research Training and Educational Component (Component I) p370-385P50DA036107 · NIDA · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI FOULDS, JONATHAN, MUSCAT, JOSHUA E · 2013 to 2018
$19.5M
LONGITUDINAL STUDY OF SCHOOL DROPOUT AND SUBSTANCE USER01DA007484 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ZIMMERMAN, MARC A · 1994 to 2011
$5.1M
Measurement and Methodology for Daily Patterns of Drug Use and Related BehaviorsR01DA035183 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BUU, YUH-PEY ANNE, CUNNINGHAM, REBECCA M. · 2013 to 2017
$2.6M
New Statistical Methodology to Establish Construct Validity for Childhook RiskK01AA016591 · NIAAA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BUU, YUH-PEY ANNE · 2009 to 2013
$762k
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 CA168676NIAAA NIH HHS K01 AA016591NIDA NIH HHS P50 DA010075NIDA NIH HHS P50 DA036107NIDA NIH HHS R01 DA007484NIDA NIH HHS R01 DA035183
6 · The paper itself

Abstract

Ordinal responses are very common in longitudinal data collected from substance abuse research or other behavioral research. This study develops a new statistical model with free SAS macros that can be applied to characterize time-varying effects on ordinal responses. Our simulation study shows that the ordinal-scale time-varying effects model has very low estimation bias and sometimes offers considerably better performance when fitting data with ordinal responses than a model that treats the response as continuous. Contrary to a common assumption that an ordinal scale with several levels can be treated as continuous, our results indicate that it is not so much the number of levels on the ordinal scale but rather the skewness of the distribution that makes a difference on relative performance of linear versus ordinal models. We use longitudinal data from a well-known study on youth at high risk for substance abuse as a motivating example to demonstrate that the proposed model can characterize the time-varying effect of negative peer influences on alcohol use in a way that is more consistent with the developmental theory and existing literature, in comparison with the linear time-varying effect model.

Indexed as

Adolescent BehaviorEpidemiologic Research DesignRisk-TakingAdolescentAdultBiasComputer SimulationFemaleHumansLinear ModelsLongitudinal StudiesMalePeer GroupProtective FactorsRisk FactorsSubstance-Related DisordersB-splinelongitudinal dataordinal responsesubstance abusetime-varying effect

Identifiers

PMID25209555
PMCPMC4227951
OpenAlexW2092595666

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.