Literature DB >> 16170656

Molecular bias.

John P A Ioannidis1.   

Abstract

Bias is ubiquitous in research. The advent of the molecular era provides a unique opportunity to study the consequences of bias with large-scale empirical evidence accumulated in the massive data produced by the current discovery-oriented scientific effort, rather than just with theoretical speculations and constructs. Here I discuss some empirical evidence about manifestations of bias in molecular epidemiology. Bias may manifest as either heterogeneity or as deviation from the true estimates. The failure to translate molecular knowledge and the failure to replicate information are some typical hallmarks of bias at action. The acquired knowledge about the behaviour and manifestations of bias in molecular fields can be transferred back also to more traditional fields of epidemiology and medical research. Getting rid of false claims of the past is at least as important as producing new scientific discoveries. In many fields, the observed effects sizes that circulate as established knowledge are practically estimating only the net bias that has operated in the field all along. Issues of plausibility (in particular biological plausibility), replication, and credibility that form the theoretical basis of epidemiology and etiological inference can now be approached with large-scale empirical data.

Mesh:

Year:  2005        PMID: 16170656     DOI: 10.1007/s10654-005-2028-1

Source DB:  PubMed          Journal:  Eur J Epidemiol        ISSN: 0393-2990            Impact factor:   8.082


  34 in total

Review 1.  Genetic associations in large versus small studies: an empirical assessment.

Authors:  John P A Ioannidis; Thomas A Trikalinos; Evangelia E Ntzani; Despina G Contopoulos-Ioannidis
Journal:  Lancet       Date:  2003-02-15       Impact factor: 79.321

Review 2.  Genetic associations: false or true?

Authors:  John P A Ioannidis
Journal:  Trends Mol Med       Date:  2003-04       Impact factor: 11.951

3.  Assessing the probability that a positive report is false: an approach for molecular epidemiology studies.

Authors:  Sholom Wacholder; Stephen Chanock; Montserrat Garcia-Closas; Laure El Ghormli; Nathaniel Rothman
Journal:  J Natl Cancer Inst       Date:  2004-03-17       Impact factor: 13.506

4.  Lessons from controversy: ovarian cancer screening and serum proteomics.

Authors:  David F Ransohoff
Journal:  J Natl Cancer Inst       Date:  2005-02-16       Impact factor: 13.506

5.  Is this clinical trial fully registered? A statement from the International Committee of Medical Journal Editors.

Authors:  Catherine D De Angelis; Jeffrey M Drazen; Frank A Frizelle; Charlotte Haug; John Hoey; Richard Horton; Sheldon Kotzin; Christine Laine; Ana Marusic; A John P M Overbeke; Torben V Schroeder; Harold C Sox; Martin B Van Der Weyden
Journal:  Ann Intern Med       Date:  2005-07-19       Impact factor: 25.391

6.  Bias in analytic research.

Authors:  D L Sackett
Journal:  J Chronic Dis       Date:  1979

7.  Selective reporting biases in cancer prognostic factor studies.

Authors:  Panayiotis A Kyzas; Konstantinos T Loizou; John P A Ioannidis
Journal:  J Natl Cancer Inst       Date:  2005-07-20       Impact factor: 13.506

8.  Commentary: meta-analysis of individual participants' data in genetic epidemiology.

Authors:  John P A Ioannidis; Philip S Rosenberg; James J Goedert; Thomas R O'Brien
Journal:  Am J Epidemiol       Date:  2002-08-01       Impact factor: 4.897

Review 9.  Predictive ability of DNA microarrays for cancer outcomes and correlates: an empirical assessment.

Authors:  Evangelia E Ntzani; John P A Ioannidis
Journal:  Lancet       Date:  2003-11-01       Impact factor: 79.321

10.  Why most published research findings are false.

Authors:  John P A Ioannidis
Journal:  PLoS Med       Date:  2005-08-30       Impact factor: 11.613

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  3 in total

1.  Developing organized level of biomedical evidence: evidence-based biomedicine.

Authors:  Fakher Rahim; Akbar Soltani; Vahid Haghpanah
Journal:  Front Physiol       Date:  2012-11-08       Impact factor: 4.566

2.  Lack of association between the Serotonin Transporter Promoter Polymorphism (5-HTTLPR) and Panic Disorder: a systematic review and meta-analysis.

Authors:  Carolina Blaya; Giovanni A Salum; Maurício S Lima; Sandra Leistner-Segal; Gisele G Manfro
Journal:  Behav Brain Funct       Date:  2007-08-18       Impact factor: 3.759

3.  Proliferative, pro-inflammatory, and angiogenesis regulator gene expression profile defines prognosis in different histopathological subtypes of nodal peripheral T-cell lymphoma.

Authors:  Luís Alberto de Pádua Covas Lage; Débora Levy; Flávia Dias Xavier; Diego Cândido Reis; Renata de Oliveira Costa; Marianne Castro Gonçalves; Vanderson Rocha; Maria Cláudia Nogueira Zerbini; Juliana Pereira
Journal:  Oncotarget       Date:  2019-08-27
  3 in total

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