Evidence map›Paper›PMID 25472674›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2014

Improving power to detect changes in blood miRNA expression by accounting for sources of variability in experimental designs.

Sarah I Daniels, Fenna C M Sillé, Audrey Goldbaum, Brenda Yee, Ellen F Key, Luoping Zhang, Martyn T Smith, Reuben Thomas

Abstract read
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Trial
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  4. Review
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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

8 authors.

Sarah I DanielsDivision of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California. sdaniels@berkeley.edu.
Fenna C M SilléDivision of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California.
Audrey GoldbaumDivision of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California.
Brenda YeeDivision of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California.
Ellen F KeyDivision of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California.
Luoping ZhangDivision of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California.
Martyn T SmithDivision of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California.
Reuben ThomasDivision of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California.

Funding

Training CoreP42ES004705 · NIEHS · UNIVERSITY OF CALIFORNIA BERKELEY · PI MCHALE, CLIONA · 1987 to 2025
$74.6M
NIEHS NIH HHS P42 ES004705NIEHS NIH HHS P42ES004705
6 · The paper itself

Abstract

backgroundBlood miRNAs are a new promising area of disease research, but variability in miRNA measurements may limit detection of true-positive findings. Here, we measured sources of miRNA variability and determine whether repeated measures can improve power to detect fold-change differences between comparison groups.

methodsBlood from healthy volunteers (N = 12) was collected at three time points. The miRNAs were extracted by a method predetermined to give the highest miRNA yield. Nine different miRNAs were quantified using different qPCR assays and analyzed using mixed models to identify sources of variability. A larger number of miRNAs from a publicly available blood miRNA microarray dataset with repeated measures were used for a bootstrapping procedure to investigate effects of repeated measures on power to detect fold changes in miRNA expression for a theoretical case-control study.

resultsTechnical variability in qPCR replicates was identified as a significant source of variability (P < 0.05) for all nine miRNAs tested. Variability was larger in the TaqMan qPCR assays (SD = 0.15-0.61) versus the qScript qPCR assays (SD = 0.08-0.14). Inter- and intraindividual and extraction variability also contributed significantly for two miRNAs. The bootstrapping procedure demonstrated that repeated measures (20%-50% of N) increased detection of a 2-fold change for approximately 10% to 45% more miRNAs.

conclusionStatistical power to detect small fold changes in blood miRNAs can be improved by accounting for sources of variability using repeated measures and choosing appropriate methods to minimize variability in miRNA quantification. IMPACT: This study demonstrates the importance of including repeated measures in experimental designs for blood miRNA research. See all the articles in this CEBP Focus section, "Biomarkers, Biospecimens, and New Technologies in Molecular Epidemiology."

Indexed as

Healthy VolunteersHumansMicroRNAsResearch DesignMicroRNAs

Identifiers

PMID25472674
PMCPMC4256675

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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.