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Kawabata T, Emoto R, Nishino J, Takahashi K, Matsui S. Two-stage analysis for selecting fixed numbers of features in omics association studies. Stat Med 2019; 38:2956-2971. [PMID: 30931544 DOI: 10.1002/sim.8150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 12/31/2018] [Accepted: 02/28/2019] [Indexed: 11/07/2022]
Abstract
One of main roles of omics-based association studies with high-throughput technologies is to screen out relevant molecular features, such as genetic variants, genes, and proteins, from a large pool of such candidate features based on their associations with the phenotype of interest. Typically, screened features are subject to validation studies using more established or conventional assays, where the number of evaluable features is relatively limited, so that there may exist a fixed number of features measurable by these assays. Such a limitation necessitates narrowing a feature set down to a fixed size, following an initial screening analysis via multiple testing where adjustment for multiplicity is made. We propose a two-stage screening approach to control the false discovery rate (FDR) for a feature set with fixed size that is subject to validation studies, rather than for a feature set from the initial screening analysis. Out of the feature set selected in the first stage with a relaxed FDR level, a fraction of features with most statistical significance is firstly selected. For the remaining feature set, features are selected based on biological consideration only, without regard to any statistical information, which allows evaluating the FDR level for the finally selected feature set with fixed size. Improvement of the power is discussed in the proposed two-stage screening approach. Simulation experiments based on parametric models and real microarray datasets demonstrated substantial increment in the number of screened features for biological consideration compared with the standard screening approach, allowing for more extensive and in-depth biological investigations in omics association studies.
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Affiliation(s)
- Takanori Kawabata
- Clinical Research Promotion Unit, Clinical Research Center, Shizuoka Cancer Center, Shizuoka, Japan
| | - Ryo Emoto
- Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Jo Nishino
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kunihiko Takahashi
- Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shigeyuki Matsui
- Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya, Japan
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Heavner K, Newschaffer C, Hertz-Picciotto I, Bennett D, Burstyn I. Pooling Bio-Specimens in the Presence of Measurement Error and Non-Linearity in Dose-Response: Simulation Study in the Context of a Birth Cohort Investigating Risk Factors for Autism Spectrum Disorders. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2015; 12:14780-99. [PMID: 26610532 PMCID: PMC4661679 DOI: 10.3390/ijerph121114780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2015] [Revised: 11/04/2015] [Accepted: 11/06/2015] [Indexed: 11/16/2022]
Abstract
We sought to determine the potential effects of pooling on power, false positive rate (FPR), and bias of the estimated associations between hypothetical environmental exposures and dichotomous autism spectrum disorders (ASD) status. Simulated birth cohorts in which ASD outcome was assumed to have been ascertained with uncertainty were created. We investigated the impact on the power of the analysis (using logistic regression) to detect true associations with exposure (X1) and the FPR for a non-causal correlate of exposure (X2, r = 0.7) for a dichotomized ASD measure when the pool size, sample size, degree of measurement error variance in exposure, strength of the true association, and shape of the exposure-response curve varied. We found that there was minimal change (bias) in the measures of association for the main effect (X1). There is some loss of power but there is less chance of detecting a false positive result for pooled compared to individual level models. The number of pools had more effect on the power and FPR than the overall sample size. This study supports the use of pooling to reduce laboratory costs while maintaining statistical efficiency in scenarios similar to the simulated prospective risk-enriched ASD cohort.
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Affiliation(s)
- Karyn Heavner
- Department of Environmental and Occupational Health, Dornsife School of Public Health, Drexel University, Philadelphia, PA 19104, USA.
| | - Craig Newschaffer
- A.J. Drexel Autism Institute, Dornsife School of Public Health, Drexel University, Philadelphia, PA 19104, USA.
| | - Irva Hertz-Picciotto
- Department of Public Health Sciences, University of California at Davis, Davis, CA 95616, USA.
| | - Deborah Bennett
- Department of Public Health Sciences, University of California at Davis, Davis, CA 95616, USA.
| | - Igor Burstyn
- Department of Environmental and Occupational Health, Dornsife School of Public Health, Drexel University, Philadelphia, PA 19104, USA.
- A.J. Drexel Autism Institute, Dornsife School of Public Health, Drexel University, Philadelphia, PA 19104, USA.
- Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA 19104, USA.
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Carichon M, Pallet N, Schmitt C, Lefebvre T, Gouya L, Talbi N, Deybach JC, Beaune P, Vasos P, Puy H, Bertho G. Urinary Metabolic Fingerprint of Acute Intermittent Porphyria Analyzed by 1H NMR Spectroscopy. Anal Chem 2014; 86:2166-74. [DOI: 10.1021/ac403837r] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Mickael Carichon
- UMRS
8601 CNRS, 75006 Paris, France
- Université Paris Descartes, Sorbonne
Paris Cité, 75006 Paris, France
| | - Nicolas Pallet
- Université Paris Descartes, Sorbonne
Paris Cité, 75006 Paris, France
- Service
de Biochimie, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, 75015 Paris, France
- Service
de Néphrologie, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, 75015 Paris, France
- INSERM
U775, Centre Universitaire des Saints Pères, 75006 Paris, France
| | - Caroline Schmitt
- Centre
Français des Porphyries, Hôpital Louis Mourier, Assistance Publique-Hôpitaux de Paris, 92700 Colombes, France
- Centre de Recherche
sur l'Inflammation (CRI)/UMR 1149 INSERM, 75018 Paris, France
- Université Paris Diderot, 75013 Paris, France
| | - Thibaud Lefebvre
- Centre
Français des Porphyries, Hôpital Louis Mourier, Assistance Publique-Hôpitaux de Paris, 92700 Colombes, France
- Centre de Recherche
sur l'Inflammation (CRI)/UMR 1149 INSERM, 75018 Paris, France
- Université Paris Diderot, 75013 Paris, France
| | - Laurent Gouya
- Centre
Français des Porphyries, Hôpital Louis Mourier, Assistance Publique-Hôpitaux de Paris, 92700 Colombes, France
- Centre de Recherche
sur l'Inflammation (CRI)/UMR 1149 INSERM, 75018 Paris, France
- Université Paris Diderot, 75013 Paris, France
| | - Neila Talbi
- Centre
Français des Porphyries, Hôpital Louis Mourier, Assistance Publique-Hôpitaux de Paris, 92700 Colombes, France
- Centre de Recherche
sur l'Inflammation (CRI)/UMR 1149 INSERM, 75018 Paris, France
- Université Paris Diderot, 75013 Paris, France
| | - Jean Charles Deybach
- Centre
Français des Porphyries, Hôpital Louis Mourier, Assistance Publique-Hôpitaux de Paris, 92700 Colombes, France
- Université Paris Diderot, 75013 Paris, France
| | - Philippe Beaune
- Université Paris Descartes, Sorbonne
Paris Cité, 75006 Paris, France
- Service
de Biochimie, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, 75015 Paris, France
- INSERM
U775, Centre Universitaire des Saints Pères, 75006 Paris, France
| | - Paul Vasos
- UMRS
8601 CNRS, 75006 Paris, France
- Université Paris Descartes, Sorbonne
Paris Cité, 75006 Paris, France
| | - Hervé Puy
- Centre
Français des Porphyries, Hôpital Louis Mourier, Assistance Publique-Hôpitaux de Paris, 92700 Colombes, France
- Centre de Recherche
sur l'Inflammation (CRI)/UMR 1149 INSERM, 75018 Paris, France
- Université Paris Diderot, 75013 Paris, France
| | - Gildas Bertho
- UMRS
8601 CNRS, 75006 Paris, France
- Université Paris Descartes, Sorbonne
Paris Cité, 75006 Paris, France
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Genomic biomarkers for personalized medicine: development and validation in clinical studies. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:865980. [PMID: 23690882 PMCID: PMC3652056 DOI: 10.1155/2013/865980] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2013] [Accepted: 03/22/2013] [Indexed: 12/26/2022]
Abstract
The establishment of high-throughput technologies has brought substantial advances to our understanding of the biology of many diseases at the molecular level and increasing expectations on the development of innovative molecularly targeted treatments and molecular biomarkers or diagnostic tests in the context of clinical studies. In this review article, we position the two critical statistical analyses of high-dimensional genomic data, gene screening and prediction, in the framework of development and validation of genomic biomarkers or signatures, through taking into consideration the possible different strategies for developing genomic signatures. A wide variety of biomarker-based clinical trial designs to assess clinical utility of a biomarker or a new treatment with a companion biomarker are also discussed.
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