Evidence map›Paper›PMID 29686744›Full record

ArticleStatistics and its interface2018

Time-varying copula models for longitudinal data.

Esra Kürüm, John Hughes, Runze Li, Saul Shiffman

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistics and its interface, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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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.

Esra KürümDepartment of Statistics, University of California Riverside, Riverside, CA 92521, USA.
John HughesDepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Runze LiDepartment of Statistics and The Methodology Center, The Pennsylvania State University, University Park, PA 16802, USA.
Saul ShiffmanDepartment of Psychology, University of Pittsburgh, Pittsburgh, PA 15260, USA.

Funding

UCSF CLINICAL AND TRANSLATIONAL SCIENCE INSTITUTEUL1RR024131 · NCRR · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI JOHNSTON, S. CLAIBORNE · 2006 to 2011
$111.2M
Women's Interagency HIV Study (WIHS-V)U01AI035004 · NIAID · MONTEFIORE MEDICAL CENTER (BRONX, NY) · PI ANASTOS, KATHRYN M., SHARMA, ANJALI · 1993 to 2018
$65.7M
Women's Interagency HIV Study (WIHS) IIIU01AI031834 · NIAID · SUNY DOWNSTATE MEDICAL CENTER · PI GUSTAFSON, DEBORAH R., MINKOFF, HOWARD · 1998 to 2018
$53.5M
WOMEN'S INTERAGENCY HIV STUDY (WIHS) ORGANIZATION AND MANAGEMENTU01AI034994 · NIAID · GEORGETOWN UNIVERSITY · PI KASSAYE, SEBLE G · 1993 to 2018
$52.0M
Women's Interagency HIV Study (WIHS) IV, Chicago ConsortiumU01AI034993 · NIAID · HEKTOEN INSTITUTE FOR MEDICAL RESEARCH · PI COHEN, MARDGE H, FRENCH, AUDREY · 1994 to 2018
$51.0M
WOMENS INTERAGENCY HIV STUDY (WIHS)U01HD032632 · NICHD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI MILAM, JOEL E · 1994 to 2012
$42.0M
WIHS DATA MANAGEMENT AND ANALYSIS CENTER(WDMAC)U01AI042590 · NIAID · JOHNS HOPKINS UNIVERSITY · PI GANGE, STEPHEN J, GOLUB, ELIZABETH T · 1998 to 2018
$38.4M
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
Pilot, Mentoring, and Professional Development CoreP50DA039838 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI LI, RUNZE · 2015 to 2019
$13.9M
Penn State Biomedical Big Data to Knowledge (B2D2K) Training ProgramT32LM012415 · NLM · PENNSYLVANIA STATE UNIVERSITY, THE · PI BROACH, JAMES R., HONAVAR, VASANT G. · 2016 to 2020
$1.2M
NCRR NIH HHS UL1 RR024131NIAID NIH HHS U01 AI031834NIAID NIH HHS U01 AI034993NIAID NIH HHS U01 AI034994NIAID NIH HHS U01 AI035004NIAID NIH HHS U01 AI042590NICHD NIH HHS U01 HD032632NIDA NIH HHS P50 DA036107NIDA NIH HHS P50 DA039838NLM NIH HHS T32 LM012415
6 · The paper itself

Abstract

We propose a copula-based joint modeling framework for mixed longitudinal responses. Our approach permits all model parameters to vary with time, and thus will enable researchers to reveal dynamic response-predictor relationships and response-response associations. We call the new class of models TIMECOP because we model dependence using a time-varying copula. We develop a one-step estimation procedure for the TIMECOP parameter vector, and also describe how to estimate standard errors. We investigate the finite sample performance of our procedure via three simulation studies, one of which shows that our procedure performs well under ignorable missingness. We also illustrate the applicability of our approach by analyzing binary and continuous responses from the Women's Interagency HIV Study and a smoking cessation program.

Indexed as

Bimodal kernelHIVJoint modelLocal regressionPrimary 62G08secondary 62H20Varying coefficient model

Identifiers

PMID29686744
PMCPMC5909848

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.