Evidence map›Paper›PMID 21422966›Full record

ArticleEpidemiology (Cambridge, Mass.)2010

Treatment of batch in the detection, calibration, and quantification of immunoassays in large-scale epidemiologic studies.

Brian W Whitcomb, Neil J Perkins, Paul S Albert, Enrique F Schisterman

Abstract readComparative Study
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Is Anti-Müllerian Hormone Associated With Fecundability? Findings From the EAGeR Trial.The Journal of clinical endocrinology and metabolism · 2015
    Trial
  4. Article
  5. Patterns of Sleep Duration and Metabolic Biomarkers Across the Menstrual Cycle.The Journal of clinical endocrinology and metabolism · 2025
    Article
  6. Article
  7. Article
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  12. 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.

Brian W WhitcombDivision of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003-9304, USA. bwhitcomb@schoolph.umass.edu
Neil J Perkins
Paul S Albert
Enrique F Schisterman

Funding

EAGeR Trial - The Effects of Aspirin in Gestation and Reproduction TrialZIAHD008795 · NICHD · EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT · PI PERKINS, NEIL · 2009 to 2025
$9.6M
ROC Curve MethodologyZIAHD008761 · NICHD · EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT · PI SCHISTERMAN, ENRIQUE F. · 2009 to 2021
$2.1M
Intramural NIH HHS ZIA HD008761
6 · The paper itself

Abstract

unlabelledMany laboratory assays measure biomarkers via a 2-stage process. Direct measurement yields relative measures that are subsequently transformed to the unit of interest by using a calibration experiment. The calibration experiment is performed within the main experiment and uses a validation set for which true values are known and relative values are measured by assays to estimate the relation between relative and absolute values. Immunoassays, polymerase chain reaction, and chromatographic approaches are among assays performed in this manner.

methodsFor studies with multiple batches, data from more than a single calibration experiment are available. Conventionally, calibration of assays based on the standard curve is performed specific to each batch; the calibration experiment from each batch is used to calibrate each batch independently. This batch-specific approach incorporates batch variability but, due to the small number of calibration measurements in each batch, may not be best suited for this purpose.

resultsMixed-effects models have been described to address interassay variability and to provide a measure of quality assurance. Conversely, when interbatch variability is negligible, a model that does not incorporate batch effect may be used to estimate an overall calibration curve.

conclusionWe explore approaches for use of calibration data in studies with many batches. Using a real data example with biomarker and outcome information, we show that risk estimates may vary depending on the calibration approach used. We demonstrate the potential for bias when using simulations. Under minimal interbatch variability, as seen in our data, conventional batch-specific calibration does not best use information available in the data and results in attenuated risk estimates.

Indexed as

Epidemiologic Research DesignEpidemiologic StudiesModels, StatisticalAbortion, SpontaneousBiomarkersCalibrationComputer SimulationFemaleGranulocyte Colony-Stimulating FactorHumansImmunoassayLimit of DetectionOdds RatioPregnancyRiskBiomarkersGranulocyte Colony-Stimulating Factor

Identifiers

PMID21422966
PMCPMC3073366

What OpenQuestion holds

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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.