Raimondi, Sara Gandini, Sara Fargnoli, Maria Concetta 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 Teresa Palmieri, Giuseppe Ribas, Gloria Stratigos, Alexander Cornelius, Lynn Motokawa, Tomonori Anno, Sumiko Helsing, Per Wong, Terence H Autier, Philippe Garcia-Borron, Jose C Little, Julian Newton-Bishop, Julia Sera, Francesco Liu, Fan Kayser, Manfred Nijsten, Tamar eng CA112243-05 S1/CA/NCI NIH HHS/ R01 CA112243/CA/NCI NIH HHS/ Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't England 2012/08/07 06:00 BMC Med Res Methodol. 2012 Aug 3;12:116. doi: 10.1186/1471-2288-12-116. ; International audience ; 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.