Evidence map›Paper›PMID 32145664›Full record

ReviewDrug and alcohol dependence2020

Tutorial in Biostatistics: The use of generalized additive models to evaluate alcohol consumption as an exposure variable.

Laura F White, Wenqing Jiang, Yicheng Ma, Kaku So-Armah, Jeffrey H Samet, Debbie M Cheng

Abstract readReview
In one paragraph

Review in Drug and alcohol dependence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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  5. Article
  6. Article
  7. Observational
  8. Article
  9. Hepatic injuries resulting from chronic alcohol abuse identified by forensics.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologie
    Article
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

6 authors.

Laura F WhiteDepartment of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118 United States. Electronic address: lfwhite@bu.edu.
Wenqing JiangDepartment of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118 United States.
Yicheng MaDepartment of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118 United States; Department of Biostatistics, University of Michigan, 1415 Washington Hts, Ann Arbor, MI 48109 United States.
Kaku So-ArmahGeneral Internal Medicine, Boston University School of Medicine and Boston Medical Center, 801 Massachusetts Ave, Boston, MA 02118 United States.
Jeffrey H SametGeneral Internal Medicine, Boston University School of Medicine and Boston Medical Center, 801 Massachusetts Ave, Boston, MA 02118 United States.
Debbie M ChengDepartment of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118 United States.

Funding

Translational ScienceP30AI042853 · NIAID · MIRIAM HOSPITAL · PI LARRY K BROWN · 1998 to 2026
$51.7M
URBAN ARCH 4/5 Russia Cohort-Targeting HIV-comorbidities with Pharmacotherapy to Reduce Alcohol and Tobacco Use in HIV-infected RussiansU01AA020780 · NIAAA · BOSTON MEDICAL CENTER · PI FREIBERG, MATTHEW S, SAMET, JEFFREY H. · 2011 to 2021
$4.4M
URBAN ARCH (2/5) Biostatistics and Data Management Core (BDM)U24AA020779 · NIAAA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI CHENG, DEBBIE M. · 2011 to 2020
$3.6M
URBAN ARCH (1/5) Administrative Coordinating Core (Admin Core)U24AA020778 · NIAAA · BOSTON MEDICAL CENTER · PI SAMET, JEFFREY H. · 2011 to 2020
$3.5M
Novel Mechanisms Driving Excess Atherosclerotic Cardiovascular Disease Risk in the Context of HIV: The Role of Liver InjuryK01HL134147 · NHLBI · BOSTON MEDICAL CENTER · PI SO-ARMAH, KAKU · 2016 to 2020
$620k
NHLBI NIH HHS K01 HL134147NIAAA NIH HHS U01 AA020780NIAAA NIH HHS U24 AA020778NIAAA NIH HHS U24 AA020779NIAID NIH HHS P30 AI042853
6 · The paper itself

Abstract

Alcohol consumption is a commonly studied risk factor for many poor health outcomes. Various instruments exist to measure alcohol consumption, including the AUDIT-C, Single Alcohol Screening Questionnaire (SASQ) and Timeline Followback. The information gathered by these instruments is often simplified and analyzed as a dichotomous measure, risking the loss of information of potentially prognostic value. We discuss generalized additive models (GAM) as a useful tool to understand the association between alcohol consumption and a health outcome. We demonstrate how this analytic strategy can guide the development of a regression model that retains maximal information about alcohol consumption. We illustrate these approaches using data from the Russia ARCH (Alcohol Research Collaboration on HIV/AIDS) study to analyze the association between alcohol consumption and biomarker of systemic inflammation, interleukin-6 (IL-6). We provide SAS and R code to implement these methods. GAMs have the potential to increase statistical power and allow for better elucidation of more nuanced and non-linear associations between alcohol consumption and important health outcomes.

Indexed as

Alcohol DrinkingBiomarkersBiostatisticsHumansInterleukin-6Linear ModelsRisk FactorsBiomarkersIL6 protein, humanInterleukin-6Alcohol consumptionAUDIT-CGeneralized additive models

Identifiers

PMID32145664
PMCPMC7171980

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.