Evidence map›Paper›PMID 26272994›Full record

ArticleBiostatistics (Oxford, England)2016

Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses.

Vegard Nygaard, Einar Andreas Rødland, Eivind Hovig

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biostatistics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 206 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
206citing papers in PubMed, 5 pooled it
–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

206 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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146 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Vegard NygaardDepartment of Tumor Biology, Institute for Cancer Research, Oslo University Hospital HF - Radiumhospitalet, Montebello, 0310 Oslo, Norway.
Einar Andreas RødlandDepartment of Tumor Biology, Institute for Cancer Research, Oslo University Hospital HF - Radiumhospitalet, Montebello, 0310 Oslo, Norway.
Eivind HovigDepartment of Tumor Biology, Institute for Cancer Research, Oslo University Hospital HF - Radiumhospitalet, Montebello, 0310 Oslo, Norway; Institute of Cancer Genetics and Informatics, Oslo University Hospital HF - Radiumhospitalet, Montebello, 0310 Oslo, Norway and Department of Informatics, University of Oslo, 0316 Oslo, Norway ehovig@ifi.uio.no.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Removal of, or adjustment for, batch effects or center differences is generally required when such effects are present in data. In particular, when preparing microarray gene expression data from multiple cohorts, array platforms, or batches for later analyses, batch effects can have confounding effects, inducing spurious differences between study groups. Many methods and tools exist for removing batch effects from data. However, when study groups are not evenly distributed across batches, actual group differences may induce apparent batch differences, in which case batch adjustments may bias, usually deflate, group differences. Some tools therefore have the option of preserving the difference between study groups, e.g. using a two-way ANOVA model to simultaneously estimate both group and batch effects. Unfortunately, this approach may systematically induce incorrect group differences in downstream analyses when groups are distributed between the batches in an unbalanced manner. The scientific community seems to be largely unaware of how this approach may lead to false discoveries.

Indexed as

Data Interpretation, StatisticalHumansMicroarray AnalysisReproducibility of ResultsBatch effectsData normalizationMicroarraysReproducible research

Identifiers

PMID26272994
PMCPMC4679072

What OpenQuestion holds

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Registered trials

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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.