ArticleClinical cancer research : an official journal of the American Association for Cancer Research2014
Blocking and randomization to improve molecular biomarker discovery.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- BatMan: Mitigating Batch Effects Via Stratification for Survival Outcome Prediction.JCO clinical cancer informatics · 2023Article
- On data normalization and batch-effect correction for tumor subtyping with microRNA data.NAR genomics and bioinformatics · 2023Article
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- Performance evaluation of transcriptomics data normalization for survival risk prediction.Briefings in bioinformatics · 2021Article
- Making External Validation Valid for Molecular Classifier Development.JCO precision oncology · 2021Article
- Biomarker Discovery and Validation: Statistical Considerations.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2021Article
- Statistical Assessment of Depth Normalization for Small RNA Sequencing.JCO clinical cancer informatics · 2020Article
- A pair of datasets for microRNA expression profiling to examine the use of careful study design for assigning arrays to samples.Scientific data · 2018Article
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- Can Peripheral Blood-Derived Gene Expressions Characterize Individuals at Ultra-high Risk for Psychosis?Computational psychiatry (Cambridge, Mass.) · 2017Article
- Cautionary Note on Using Cross-Validation for Molecular Classification.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2016Article
- Study design and data analysis considerations for the discovery of prognostic molecular biomarkers: a case study of progression free survival in advanced serous ovarian cancer.BMC medical genomics · 2016Article
- Measurement of glycosylated alpha-fetoprotein improves diagnostic power over the native form in hepatocellular carcinoma.PloS one · 2014Article
- Preprocessing Steps for Agilent MicroRNA Arrays: Does the Order Matter?Cancer informatics · 2014Article
- MicroRNA array normalization: an evaluation using a randomized dataset as the benchmark.PloS one · 2014Article
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9 authors.
Funding
Abstract
Randomization and blocking have the potential to prevent the negative impacts of nonbiologic effects on molecular biomarker discovery. Their use in practice, however, has been scarce. To demonstrate the logistic feasibility and scientific benefits of randomization and blocking, we conducted a microRNA study of endometrial tumors (n = 96) and ovarian tumors (n = 96) using a blocked randomization design to control for nonbiologic effects; we profiled the same set of tumors for a second time using no blocking or randomization. We assessed empirical evidence of differential expression in the two studies. We performed simulations through virtual rehybridizations to further evaluate the effects of blocking and randomization. There was moderate and asymmetric differential expression (351/3,523, 10%) between endometrial and ovarian tumors in the randomized dataset. Nonbiologic effects were observed in the nonrandomized dataset, and 1,934 markers (55%) were called differentially expressed. Among them, 185 were deemed differentially expressed (185/351, 53%) and 1,749 not differentially expressed (1,749/3,172, 55%) in the randomized dataset. In simulations, when randomization was applied to all samples at once or within batches of samples balanced in tumor groups, blocking improved the true-positive rate from 0.95 to 0.97 and the false-positive rate from 0.02 to 0.002; when sample batches were unbalanced, randomization was associated with the true-positive rate (0.92) and the false-positive rate (0.10) regardless of blocking. Normalization improved the detection of true-positive markers but still retained sizeable false-positive markers. Randomization and blocking should be used in practice to more fully reap the benefits of genomics technologies.
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