ArticleAmerican journal of epidemiology2013
New models for large prospective studies: is there a risk of throwing out the baby with the bathwater?
Article in American journal of epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Big data, observational research and P-value: a recipe for false-positive findings? A study of simulated and real prospective cohorts.International journal of epidemiology · 2020Article
- Article
- A randomised controlled trial comparing opt-in and opt-out home visits for tracing lost participants in a prospective birth cohort study.BMC medical research methodology · 2015Article
- Invited commentary: future of population studies--defining research priorities and processes.American journal of epidemiology · 2015Article
- Community engagement for big epidemiology: deliberative democracy as a tool.Journal of personalized medicine · 2014Article
- Identifying the appropriate comparison group for HIV-infected individuals.Current opinion in HIV and AIDS · 2014Review
- Newspaper coverage of biobanks.PeerJ · 2014Article
- Vehement agreement on new models?American journal of epidemiology · 2013Article
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Authors and funding
20 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.