Melanocortin-1 receptor, skin cancer and phenotypic characteristics (M-SKIP) project: study design and methods for pooling results of genetic epidemiological studies

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Title: Melanocortin-1 receptor, skin cancer and phenotypic characteristics (M-SKIP) project: study design and methods for pooling results of genetic epidemiological studies
Authors: Raimondi, Sara
Gandini, Sara
Fargnoli, Maria C
Bagnardi, Vincenzo
Maisonneuve, Patrick
Specchia, Claudia
Kumar, Rajiv
Nagore, Eduardo
Han, Jiali
Hansson, Johan
Kanetsky, Peter A
Ghiorzo, Paola
Gruis, Nelleke A
Dwyer, Terry
Blizzard, Leigh
Fernandez-de-Misa, Ricardo
Branicki, Wojciech
Debniak, Tadeusz
Morling, Niels
Landi, Maria T
Palmieri, Giuseppe
Ribas, Gloria
Stratigos, Alexander
Cornelius, Lynn
Motokawa, Tomonori
Anno, Sumiko
Helsing, Per
Wong, Terence H
Autier, Philippe
García-Borrón, José C
Little, Julian
Newton-Bishop, Julia
Sera, Francesco
Liu, Fan
Kayser, Manfred
Nijsten, Tamar
Date: 2012-08-03
Abstract: Abstract Background 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. Discussion 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.
URL: http://dx.doi.org/10.1186/1471-2288-12-116
http://hdl.handle.net/10393/33600
CollectionLibre accès - Publications // Open Access - Publications
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