Evidence map›Paper›PMID 26071614›Full record

ArticleJournal of immunological methods2015

Analysis of liquid bead microarray antibody assay data for epidemiologic studies of pathogen-cancer associations.

Danny V Colombara, James P Hughes, Andrea N Burnett-Hartman, Stephen E Hawes, Denise A Galloway, Stephen M Schwartz, Roberd M Bostick, John D Potter, Lisa E Manhart

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of immunological methods, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Danny V ColombaraPublic Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., P.O. Box 19024, Seattle, WA 98109-1024, USA; Department of Epidemiology, School of Public Health, University of Washington, F-263 Health Sciences Building, Box 357236, Seattle, WA 98195-7236, USA. Electronic address: dvc2@uw.edu.
James P HughesPublic Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., P.O. Box 19024, Seattle, WA 98109-1024, USA; Department of Biostatistics, School of Public Health, University of Washington, F-600 Health Sciences Building, Box 357232, Seattle, WA 98195-7232, USA.
Andrea N Burnett-HartmanPublic Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., P.O. Box 19024, Seattle, WA 98109-1024, USA; Department of Epidemiology, School of Public Health, University of Washington, F-263 Health Sciences Building, Box 357236, Seattle, WA 98195-7236, USA.
Stephen E HawesDepartment of Epidemiology, School of Public Health, University of Washington, F-263 Health Sciences Building, Box 357236, Seattle, WA 98195-7236, USA.
Denise A GallowayHuman Biology Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., P.O. Box 19024, Seattle, WA 98109-1024, USA; Department of Microbiology, School of Medicine, University of Washington, Box 357735, Seattle, WA 98195-7735, USA.
Stephen M SchwartzPublic Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., P.O. Box 19024, Seattle, WA 98109-1024, USA; Department of Epidemiology, School of Public Health, University of Washington, F-263 Health Sciences Building, Box 357236, Seattle, WA 98195-7236, USA.
Roberd M BostickDepartment of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Road, NE, Atlanta, GA 30322, USA; Winship Cancer Institute, Emory University, 1365-C Clifton Road NE, Atlanta, GA 30322, USA.
John D PotterPublic Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., P.O. Box 19024, Seattle, WA 98109-1024, USA; Department of Epidemiology, School of Public Health, University of Washington, F-263 Health Sciences Building, Box 357236, Seattle, WA 98195-7236, USA; Centre for Public Health Research, Massey University, PO Box 756, Wellington 6140, New Zealand.
Lisa E ManhartDepartment of Epidemiology, School of Public Health, University of Washington, F-263 Health Sciences Building, Box 357236, Seattle, WA 98195-7236, USA; University of Washington Center for AIDS and STD, 325 9th Ave, Campus Box 359931, Seattle, WA 98104, USA.

Funding

Institute of Translational Health SciencesKL2TR000421 · NCATS · UNIVERSITY OF WASHINGTON · PI DISIS, MARY L. · 2012 to 2016
$12.6M
Cancer Prevention Trng-Nutrition, Exercise &GeneticsR25CA094880 · NCI · UNIVERSITY OF WASHINGTON · PI WHITE, EMILY · 2002 to 2016
$7.3M
COLON CANCER PREVENTION RESEARCH UNITP01CA050305 · NCI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI ELMER, PATRICIA J · 1990 to 1994
–
NCATS NIH HHS KL2 TR000421NCATS NIH HHS KL2TR00421NCI NIH HHS P01 CA050305NCI NIH HHS R25 CA094880NCI NIH HHS R25CA094880
6 · The paper itself

Abstract

backgroundLiquid bead microarray antibody (LBMA) assays are used to assess pathogen-cancer associations. However, studies analyze LBMA data differently, limiting comparability.

methodsWe generated 10,000 Monte Carlo-type simulations of log-normal antibody distributions (exposure) with 200 cases and 200 controls (outcome). We estimated type I error rates, statistical power, and bias associated with t-tests, logistic regression with a linear exposure and with the exposure dichotomized at 200 units, 400 units, the mean among controls plus two standard deviations, and the value corresponding to the optimal sensitivity and specificity. We also applied these models, and data visualizations (kernel density plots, receiver operating characteristic (ROC) curves, predicted probability plots, and Q-Q plots), to two empirical datasets to assess the consistency of the exposure-outcome relationship.

resultsAll strategies had acceptable type I error rates (0.03 ≤ P ≤ 0.048), except for the dichotomization according to optimal sensitivity and specificity, which had a type I error rate of 0.27. Among the remaining methods, logistic regression with a linear predictor (Power=1.00) and t-tests (Power=1.00) had the highest power to detect a mean difference of 1.0 MFI (median fluorescence intensity) on the log scale and were unbiased. Dichotomization methods upwardly biased the risk estimates.

conclusionThese results indicate that logistic regression with linear predictors and unpaired t-tests are superior to logistic regression with dichotomized predictors for assessing disease associations with LBMA data. Logistic regression with continuous linear predictors and t-tests are preferable to commonly used LBMA dichotomization methods.

Indexed as

AntibodiesBiological AssayCase-Control StudiesData Interpretation, StatisticalEnvironmental MonitoringEpidemiologic StudiesHumansModels, StatisticalModels, TheoreticalMonte Carlo MethodNeoplasmsSensitivity and SpecificityAntibodiesCut-pointDichotomizationLiquid bead microarray antibody assayMedian fluorescence intensityVisualization

Identifiers

PMID26071614
PMCPMC4604026

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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.