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Methods for individual participant data meta-analysis of survival outcomes commonly focus on the hazard ratio as a measure of treatment effect. Recently, Siannis et al. (2010, Statistics in Medicine 29:3030-3045) proposed the use of percentile ratios as an alternative to hazard ratios. We describe a novel two-stage method for the meta-analysis of percentile ratios that avoids distributional assumptions at the study level. © 2012 John Wiley & Sons, Ltd.

Genome-wide association studies (GWAS) have led to a rapid increase in available data on common genetic variants and phenotypes and numerous discoveries of new loci associated with susceptibility to common complex diseases. Integrating the evidence from GWAS and candidate gene studies depends on concerted efforts in data production, online publication, database development, and continuously updated data synthesis. Here the authors summarize current experience and challenges on these fronts, which were discussed at a 2008 multidisciplinary workshop sponsored by the Human Genome Epidemiology Network. Comprehensive field synopses that integrate many reported gene-disease associations have been systematically developed for several fields, including Alzheimer's disease, schizophrenia, bladder cancer, coronary heart disease, preterm birth, and DNA repair genes in various cancers. The authors summarize insights from these field synopses and discuss remaining unresolved issues - especially in the light of evidence from GWAS, for which they summarize empirical P-value and effect-size data on 223 discovered associations for binary outcomes (142 with P

The Human Genome Epidemiology Network (HuGENet) recently launched a global network of consortia working on human genome epidemiology. This Network of Investigator Networks aims to create a resource to share information, offer methodologic support, generate inclusive overviews of studies conducted in specific fields, and to facilitate rapid confirmation of findings. In October 2005, HuGENet brought together representatives from established and emerging networks in order to share their experiences at a workshop in Cambridge, United Kingdom. In advance of the meeting, a qualitative questionnaire was distributed to workshop participants. The questionnaire elicited information on experiences and practices in building and maintaining consortia. This chapter reports on the numerous challenges and their possible solutions as identified by the workshop participants, as well as new opportunities offered by the network approach to genetic and genomic epidemiology.

The task of identifying genetic determinants for complex, multigenetic diseases is hampered by small studies, publication and reporting biases, and lack of common standards worldwide. The authors propose the creation of a network of networks that include groups of investigators collecting data for human genome epidemiology research. Twenty-three networks of investigators addressing specific diseases or research topics and representing several hundreds of teams have already joined this initiative. For each field, the authors are currently creating a core registry of teams already participating in the respective network. A wider international registry will include all other teams also working in the same field. Independent investigators are invited to join the registries and existing networks and to join forces in creating additional ones as needed. The network of networks aims to register these networks, teams, and investigators; be a resource for information about or connections to the many networks; offer methodological support; promote sound design and standardization of analytical practices; generate inclusive overviews of fields at large; facilitate rapid confirmation of findings; and avoid duplication of effort. Copyright © 2005 by the Johns Hopkins Bloomberg School of Public Health All rights reserved.

Established guidelines for causal inference in epidemiological studies may be inappropriate for genetic associations. A consensus process was used to develop guidance criteria for assessing cumulative epidemiologic evidence in genetic associations. A proposed semi-quantitative index assigns three levels for the amount of evidence, extent of replication, and protection from bias, and also generates a composite assessment of 'strong', 'moderate' or 'weak' epidemiological credibility. In addition, we discuss how additional input and guidance can be derived from biological data. Future empirical research and consensus development are needed to develop an integrated model for combining epidemiological and biological evidence in the rapidly evolving field of investigation of genetic factors. This work was partly supported by ECNIS (Environmental Cancer Risk, Nutrition and Individual Susceptibility), a network of excellence operating within the European Union 6th Framework Program (Contract No 513943). This research was also supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics and National Institute of Allergy and Infectious Diseases, Laboratory of Molecular Immunology. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government.