Literature DB >> 21749975

Invited commentary: consolidating data harmonization--how to obtain quality and applicability?

Isabel Fortier1, Dany Doiron, Paul Burton, Parminder Raina.   

Abstract

It is recognized that very large sample sizes capable of providing adequate statistical power are required to properly investigate and understand the role and interaction of genetic, lifestyle, and environmental factors in modulating the risk and progression of chronic diseases. However, very few one-off studies provide access to very large numbers of participants, and the collection of high-quality data necessitates a major investment of resources. The scientific community is thus increasingly engaged in collaborative efforts to facilitate harmonization and synthesis of data across studies. Complementary harmonization approaches may be adopted to support these efforts. In the current issue of the American Journal of Epidemiology, Hamilton et al. (Am J Epidemiol. 2011;174(3):253-260) present the consensus measures for Phenotypes and eXposures (PhenX) Toolkit, which promotes the use of identical data collection tools and procedures to support harmonization across emerging studies. Data synthesis is greatly facilitated by the use of common measures and procedures. However, the "stringent" criteria required by PhenX can limit its utilization. The opportunity to make use of rigorous but more "flexible" harmonization approaches should also be considered.

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Year:  2011        PMID: 21749975     DOI: 10.1093/aje/kwr194

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  35 in total

Review 1.  Use of data from multiple registries in studying biologic discontinuation: challenges and opportunities.

Authors:  Kazuki Yoshida; Helga Radner; Arthur Kavanaugh; Yoon-Kyoung Sung; Sang-Cheol Bae; Mitsumasa Kishimoto; Kazuo Matsui; Masato Okada; Shigeto Tohma; Michael E Weinblatt; Daniel H Solomon
Journal:  Clin Exp Rheumatol       Date:  2013-10-03       Impact factor: 4.473

2.  Smoking, Alcohol, and Biliary Tract Cancer Risk: A Pooling Project of 26 Prospective Studies.

Authors:  Emma E McGee; Sarah S Jackson; Jessica L Petrick; Alison L Van Dyke; Hans-Olov Adami; Demetrius Albanes; Gabriella Andreotti; Laura E Beane-Freeman; Amy Berrington de Gonzalez; Julie E Buring; Andrew T Chan; Yu Chen; Gary E Fraser; Neal D Freedman; Yu-Tang Gao; Susan M Gapstur; J Michael Gaziano; Graham G Giles; Eric J Grant; Francine Grodstein; Patricia Hartge; Mazda Jenab; Cari M Kitahara; Synnove F Knutsen; Woon-Puay Koh; Susanna C Larsson; I-Min Lee; Linda M Liao; Juhua Luo; Roger L Milne; Kristine R Monroe; Marian L Neuhouser; Katie M O'Brien; Ulrike Peters; Jenny N Poynter; Mark P Purdue; Kim Robien; Dale P Sandler; Norie Sawada; Catherine Schairer; Howard D Sesso; Tracey G Simon; Rashmi Sinha; Rachael Stolzenberg-Solomon; Shoichiro Tsugane; Renwei Wang; Elisabete Weiderpass; Stephanie J Weinstein; Emily White; Alicja Wolk; Jian-Min Yuan; Anne Zeleniuch-Jacquotte; Xuehong Zhang; Bin Zhu; Katherine A McGlynn; Peter T Campbell; Jill Koshiol
Journal:  J Natl Cancer Inst       Date:  2019-12-01       Impact factor: 13.506

3.  Collaborative, pooled and harmonized study designs for epidemiologic research: challenges and opportunities.

Authors:  Catherine R Lesko; Lisa P Jacobson; Keri N Althoff; Alison G Abraham; Stephen J Gange; Richard D Moore; Sharada Modur; Bryan Lau
Journal:  Int J Epidemiol       Date:  2018-04-01       Impact factor: 7.196

Review 4.  Ovarian cancer epidemiology in the era of collaborative team science.

Authors:  Rikki A Cannioto; Britton Trabert; Elizabeth M Poole; Joellen M Schildkraut
Journal:  Cancer Causes Control       Date:  2017-03-10       Impact factor: 2.506

Review 5.  Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.

Authors:  David C Mohr; Mi Zhang; Stephen M Schueller
Journal:  Annu Rev Clin Psychol       Date:  2017-03-17       Impact factor: 18.561

6.  An Algorithm for Creating Virtual Controls Using Integrated and Harmonized Longitudinal Data.

Authors:  William B Hansen; Shyh-Huei Chen; Santiago Saldana; Edward H Ip
Journal:  Eval Health Prof       Date:  2018-05-03       Impact factor: 2.651

7.  Statistical approaches to harmonize data on cognitive measures in systematic reviews are rarely reported.

Authors:  Lauren E Griffith; Edwin van den Heuvel; Isabel Fortier; Nazmul Sohel; Scott M Hofer; Hélène Payette; Christina Wolfson; Sylvie Belleville; Meghan Kenny; Dany Doiron; Parminder Raina
Journal:  J Clin Epidemiol       Date:  2014-12-08       Impact factor: 6.437

8.  Folic acid supplementation use and the MTHFR C677T polymorphism in orofacial clefts etiology: An individual participant data pooled-analysis.

Authors:  Azeez Butali; Julian Little; Cécile Chevrier; Sylvian Cordier; Regine Steegers-Theunissen; Astanand Jugessur; Bola Oladugba; Peter A Mossey
Journal:  Birth Defects Res A Clin Mol Teratol       Date:  2013-05-13

9.  Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership.

Authors:  F FitzHenry; F S Resnic; S L Robbins; J Denton; L Nookala; D Meeker; L Ohno-Machado; M E Matheny
Journal:  Appl Clin Inform       Date:  2015-08-26       Impact factor: 2.342

10.  Education and risk of coronary heart disease: assessment of mediation by behavioral risk factors using the additive hazards model.

Authors:  Helene Nordahl; Naja Hulvej Rod; Birgitte Lidegaard Frederiksen; Ingelise Andersen; Theis Lange; Finn Diderichsen; Eva Prescott; Kim Overvad; Merete Osler
Journal:  Eur J Epidemiol       Date:  2012-11-20       Impact factor: 8.082

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