New Models for Large Prospective Studies: Is There a Risk of Throwing Out the Baby With the Bathwater?
American journal of epidemiology (Impact Factor: 5.23). 01/2013; 177(4). DOI: 10.1093/aje/kws408
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.
Article: Vehement Agreement on New Models?American journal of epidemiology 01/2013; 177(4). DOI:10.1093/aje/kws410 · 5.23 Impact Factor
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ABSTRACT: Purpose of review: HIV-infected individuals are living longer as a result of effective treatment. Age-related comorbidities now account for the majority of morbidity and mortality among treated HIV-infected adults. Previous findings regarding the age at, and risk of, these comorbidities have been mixed, sparking debate in the field. Discerning potential differences in the occurrence and burden of age-related comorbidities among treated HIV-infected adults as compared with uninfected adults of the same age requires careful selection of the appropriate uninfected comparison group. Recent findings: The validity of comparisons with HIV-uninfected populations is threatened when differences in demographic, clinical, and lifestyle characteristics between HIV-infected and uninfected adults are not considered. Identifying a pool of HIV-uninfected individuals from existing secondary data resources and employing selection methodologies may be a novel approach to reduce threats to internal validity. Issues related to identifying data sources, understanding inclusion criteria, determining measurement error, and threats to inference are discussed. Summary: The development of clinical interventions targeting age-related comorbidities will rely on deriving valid inferences from appropriate comparison groups. The use of secondary data resources and selection methodology to create the appropriate uninfected comparison group is an attractive approach in the setting of finite resources, but are not without limitations.Current Opinion in HIV and AIDS 05/2014; 9(4). DOI:10.1097/COH.0000000000000063 · 4.68 Impact Factor
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