Consortia in Cancer Epidemiology: Lessons from InterLymph

University of California, Los Angeles, Los Ángeles, California, United States
Cancer Epidemiology Biomarkers & Prevention (Impact Factor: 4.13). 03/2007; 16(2):197-9. DOI: 10.1158/1055-9965.EPI-06-0786
Source: PubMed
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    • "Progress toward understanding the genetic and environmental underpinnings of health and disease can be accelerated by international, multi-registry collaborative research. Our vision for the INTR builds upon the fundamental principles underlying the harmonization of large-scale biobanks (Harris et al., 2012) and consortium studies (Boffetta et al., 2007). It requires openness, sharing, and synergy. "
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    ABSTRACT: The International Network of Twin Registries (INTR) aims to foster scientific collaboration and promote twin research on a global scale by working to expand the resources of twin registries around the world and make them available to researchers who adhere to established guidelines for international collaboration. Our vision is to create an unprecedented scientific network of twin registries that will advance knowledge in ways that are impossible for individual registries, and includes the harmonization of data. INTR will also promote a broad range of activities, including the development of a website, formulation of data harmonization protocols, creation of a library of software tools for twin studies, design of a search engine to identify research partners, establishment of searchable inventories of data and biospecimens, development of templates for informed consent and data sharing, organization of symposia at International Society of Twin Studies conferences, support for scholar exchanges, and writing grant proposals.
    Twin Research and Human Genetics 12/2014; 17(6):574-7. DOI:10.1017/thg.2014.67 · 2.30 Impact Factor
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    • "Subsequently, over the ensuing two decades, tremendous efforts have been made to understand the risk factors accounting for the increase of NHL particularly as related to potential environmental and lifestyle risk factors. This effort has been aided by the initiation of several consortia, including a large international consortium of case-control studies (InterLymph) that has enabled a closer examination of NHL subtype-specific associations and the potential for etiologic heterogeneity as well as the assessment of less prevalent exposures [1] [6]. Moreover, this research effort has been aided by the development of a uniform classification system for lymphoid neoplasms for use in epidemiologic "
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    ABSTRACT: The incidence rates of non-Hodgkin lymphoma (NHL) have steadily increased over the last several decades in the United States, and the temporal trends in incidence can only be partially explained by the HIV epidemic. In 1992, an international workshop sponsored by the United States National Cancer Institute concluded that there was an "emerging epidemic" of NHL and emphasized the need to investigate the factors responsible for the increasing incidence of this disease. Over the past two decades, numerous epidemiological studies have examined the risk factors for NHL, particularly for putative environmental and lifestyle risk factors, and international consortia have been established in order to investigate rare exposures and NHL subtype-specific associations. While few consistent risk factors for NHL aside from immunosuppression and certain infectious agents have emerged, suggestive associations with several lifestyle and environmental factors have been reported in epidemiologic studies. Further, increasing evidence has suggested that the effects of these and other exposures may be limited to or stronger for particular NHL subtypes. This paper examines the progress that has been made over the last twenty years in elucidating the etiology of NHL, with a primary emphasis on lifestyle factors and environmental exposures.
    Journal of Cancer Epidemiology 09/2012; 2012(6):978930. DOI:10.1155/2012/978930
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    • "It also allows investigators to better control for confounding factors, evaluate alternative genetic models and estimate the joint effect of multiple genes. Finally, population-specific effect and gene-gene and gene-environment interactions may be better assessed using pooled-analysis [12]. The pooling of data from observational studies has become more common recently, and different approaches of data analysis have been applied [13]. "
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    ABSTRACT: For complex diseases like cancer, pooled-analysis of individual data represents a powerful tool to investigate the joint contribution of genetic, phenotypic and environmental factors to the development of a disease. Pooled-analysis of epidemiological studies has many advantages over meta-analysis, and preliminary results may be obtained faster and with lower costs than with prospective consortia. Design and methods Based on our experience with the study design of the Melanocortin-1 receptor (MC1R) gene, SKin cancer and Phenotypic characteristics (M-SKIP) project, we describe the most important steps in planning and conducting a pooled-analysis of genetic epidemiological studies. We then present the statistical analysis plan that we are going to apply, giving particular attention to methods of analysis recently proposed to account for between-study heterogeneity and to explore the joint contribution of genetic, phenotypic and environmental factors in the development of a disease. Within the M-SKIP project, data on 10,959 skin cancer cases and 14,785 controls from 31 international investigators were checked for quality and recoded for standardization. We first proposed to fit the aggregated data with random-effects logistic regression models. However, for the M-SKIP project, a two-stage analysis will be preferred to overcome the problem regarding the availability of different study covariates. The joint contribution of MC1R variants and phenotypic characteristics to skin cancer development will be studied via logic regression modeling. Methodological guidelines to correctly design and conduct pooled-analyses are needed to facilitate application of such methods, thus providing a better summary of the actual findings on specific fields.
    BMC Medical Research Methodology 08/2012; 12(1):116. DOI:10.1186/1471-2288-12-116 · 2.27 Impact Factor
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