Evidence map›Paper›PMID 26575519›Full record

ArticleBiometrics2016

Bayes and empirical Bayes methods for reduced rank regression models in matched case-control studies.

Jaya M Satagopan, Ananda Sen, Qin Zhou, Qing Lan, Nathaniel Rothman, Hilde Langseth, Lawrence S Engel

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 24% 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

3 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

7 authors at 1 institution in 1 country.

Jaya M SatagopanDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York 10017, U.S.A.
Ananda SenDepartments of Family Medicine and Biostatistics, University of Michigan, Ann Arbor, Michigan 48104, U.S.A.
Qin ZhouDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York 10017, U.S.A.
Qing LanDivision of Cancer Epidemiology and Genetics, National Cancer Insitute, Rockville, Maryland 20892, U.S.A.
Nathaniel RothmanDivision of Cancer Epidemiology and Genetics, National Cancer Insitute, Rockville, Maryland 20892, U.S.A.
Hilde LangsethCancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo 0304, Norway.
Lawrence S EngelDepartment of Epidemiology, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A.
Cancer Registry of Norway · NO

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
Virology Research Program (Program 4)P30CA016086 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI David Darr · 1985 to 2026
$201.5M
Disparities in COVID Disease Severity and Outcomes in New York CityUL1TR002384 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI JULIANNE L IMPERATO-MCGINLEY · 2017 to 2026
$86.2M
CTSA INFRASTRUCTURE FOR PEDIATRIC RESEARCHUL1RR024996 · NCRR · WEILL MEDICAL COLL OF CORNELL UNIV · PI IMPERATO-MCGINLEY, JULIANNE L · 2007 to 2011
$49.6M
Translational Research in GI CancerP50CA130810 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BRENNER, DEAN E. · 2010 to 2016
$11.8M
STUDY OF EXPOSURES, BEHAVIORS, AND BIOMARKERS IN CANCER EPIDEMIOLOGYR01CA137420 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI SATAGOPAN, JAYA M · 2009 to 2012
$1.6M
NCATS NIH HHS UL1 TR002384NCI NIH HHS P30 CA008748NCI NIH HHS P30 CA016086NCI NIH HHS P50 CA130810NCI NIH HHS R01 CA137420NCRR NIH HHS UL1 RR024996
6 · The paper itself

Abstract

Matched case-control studies are popular designs used in epidemiology for assessing the effects of exposures on binary traits. Modern studies increasingly enjoy the ability to examine a large number of exposures in a comprehensive manner. However, several risk factors often tend to be related in a nontrivial way, undermining efforts to identify the risk factors using standard analytic methods due to inflated type-I errors and possible masking of effects. Epidemiologists often use data reduction techniques by grouping the prognostic factors using a thematic approach, with themes deriving from biological considerations. We propose shrinkage-type estimators based on Bayesian penalization methods to estimate the effects of the risk factors using these themes. The properties of the estimators are examined using extensive simulations. The methodology is illustrated using data from a matched case-control study of polychlorinated biphenyls in relation to the etiology of non-Hodgkin's lymphoma.

Indexed as

Case-Control StudiesModels, StatisticalBiometryComputer SimulationData Interpretation, StatisticalHumansLymphoma, Non-HodgkinPolychlorinated BiphenylsRegression AnalysisPolychlorinated BiphenylsBayesian lassoBayesian ridgeEmpirical BayesHierarchical BayesNon-Hodgkin's lymphomaTwo-stage lasso

Identifiers

PMID26575519
PMCPMC4870158
OpenAlexW2179234772

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.