Independent validation test of the vote-counting strategy used to rank biomarkers from published studies

Vote counting is frequently used in meta-analyses to rank biomarker candidates, but to our knowledge, there have been no independent assessments of its validity. Here, we used predictions from a recent meta-analysis to determine how well number of supporting studies, combined sample size and mean fo...

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Bibliographic Details
Published in:Biomarkers in medicine 2015-08, Vol.9 (8), p.751-761
Main Authors: Rikke, Brad A, Wynes, Murry W, Rozeboom, Leslie M, Barón, Anna E, Hirsch, Fred R
Format: Article
Language:English
Subjects:
Adenocarcinoma - diagnosis
Adenocarcinoma - genetics
Aged
Analysis
Biological markers
Biomarkers
Biomarkers, Tumor - genetics
Candidates
Carcinoma, Non-Small-Cell Lung - diagnosis
Carcinoma, Non-Small-Cell Lung - genetics
Carcinoma, Squamous Cell - diagnosis
Carcinoma, Squamous Cell - genetics
Diagnosis
Diagnosis, Differential
Female
Humans
Lung cancer
Lung Neoplasms - diagnosis
Lung Neoplasms - genetics
Male
Messenger RNA
meta-analysis
Meta-Analysis as Topic
Methods
MicroRNA
microRNAs
MicroRNAs - genetics
Middle Aged
miRNAs
Reproducibility of Results
Reverse Transcriptase Polymerase Chain Reaction
Sample Size
Sensitivity and Specificity
statistical methods
Studies
Values
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Summary:Vote counting is frequently used in meta-analyses to rank biomarker candidates, but to our knowledge, there have been no independent assessments of its validity. Here, we used predictions from a recent meta-analysis to determine how well number of supporting studies, combined sample size and mean fold change performed as vote-counting strategy criteria. Fifty miRNAs previously ranked for their ability to distinguish lung cancer tissue from normal were assayed by RT-qPCR using 45 paired tumor-normal samples. Number of supporting studies predicted biomarker performance (p = 0.0006; r = 0.44), but sample size and fold change did not (p > 0.2). Despite limitations, counting the number supporting studies appears to be an effective criterion for ranking biomarkers. Predictions based on sample size and fold change provided little added value. External validation studies should be conducted to establish the performance characteristics of strategies used to rank biomarkers.
ISSN:1752-0363
1752-0371
DOI:10.2217/BMM.15.39