Literature DB >> 23296354

New models for large prospective studies: is there a risk of throwing out the baby with the bathwater?

Michael B Bracken1, Dean Baker, Jane A Cauley, Christina Chambers, Jennifer Culhane, Dana Dabelea, Dorr Dearborn, Carolyn D Drews-Botsch, Donald J Dudley, Maureen Durkin, Barbara Entwisle, Louise Flick, Daniel Hale, Jane Holl, Melbourne Hovell, Mark Hudak, Nigel Paneth, Bonny Specker, Mari Wilhelm, Sharon Wyatt.   

Abstract

Manolio et al. (Am J Epidemiol. 2012;175:859-866) proposed that large cohort studies adopt novel models using "temporary assessment centers" to enroll up to a million participants to answer research questions about rare diseases and "harmonize" clinical endpoints collected from administrative records. Extreme selection bias, we are told, will not harm internal validity, and "process expertise to maximize efficiency of high-throughput operations is as important as scientific rigor" (p. 861). In this article, we describe serious deficiencies in this model as applied to the United States. Key points include: 1) the need for more, not less, specification of disease endpoints; 2) the limited utility of data collected from existing administrative and clinical databases; and 3) the value of university-based centers in providing scientific expertise and achieving high recruitment and retention rates through community and healthcare provider engagement. Careful definition of sampling frames and high response rates are crucial to avoid bias and ensure inclusion of important subpopulations, especially the medically underserved. Prospective hypotheses are essential to refine study design, determine sample size, develop pertinent data collection protocols, and achieve alliances with participants and communities. It is premature to reject the strengths of large national cohort studies in favor of a new model for which evidence of efficiency is insufficient.

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Year:  2013        PMID: 23296354     DOI: 10.1093/aje/kws408

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


  7 in total

1.  Invited commentary: future of population studies--defining research priorities and processes.

Authors:  Ramachandran S Vasan; Aaron R Folsom
Journal:  Am J Epidemiol       Date:  2015-03-04       Impact factor: 4.897

Review 2.  Identifying the appropriate comparison group for HIV-infected individuals.

Authors:  Cherise Wong; Keri Althoff; Stephen J Gange
Journal:  Curr Opin HIV AIDS       Date:  2014-07       Impact factor: 4.283

3.  Comparison of Recruitment Strategy Outcomes in the National Children's Study.

Authors:  Christina H Park; Marianne Winglee; Jennifer Kwan; Linda Andrews; Mark L Hudak
Journal:  Pediatrics       Date:  2017-08       Impact factor: 7.124

4.  Vehement agreement on new models?

Authors:  Teri A Manolio; Rory Collins
Journal:  Am J Epidemiol       Date:  2013-01-07       Impact factor: 4.897

5.  A randomised controlled trial comparing opt-in and opt-out home visits for tracing lost participants in a prospective birth cohort study.

Authors:  Isabelle Bray; Sian Noble; Andy Boyd; Lindsey Brown; Pei Hayes; Joanne Malcolm; Ross Robinson; Rachel Williams; Kirsty Burston; John Macleod; Lynn Molloy; Kate Tilling
Journal:  BMC Med Res Methodol       Date:  2015-07-24       Impact factor: 4.615

6.  Community engagement for big epidemiology: deliberative democracy as a tool.

Authors:  Rebekah E McWhirter; Christine R Critchley; Dianne Nicol; Don Chalmers; Tess Whitton; Margaret Otlowski; Michael M Burgess; Joanne L Dickinson
Journal:  J Pers Med       Date:  2014-11-20

7.  Newspaper coverage of biobanks.

Authors:  Ubaka Ogbogu; Maeghan Toews; Adam Ollenberger; Pascal Borry; Helene Nobile; Manuela Bergmann; Timothy Caulfield
Journal:  PeerJ       Date:  2014-07-31       Impact factor: 2.984

  7 in total

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