Journal Article

Statistical Issues in Life Course Epidemiology

Bianca L. De Stavola, Dorothea Nitsch, Isabel dos Santos Silva, Valerie McCormack, Rebecca Hardy, Vera Mann, Tim J. Cole, Susan Morton and David A. Leon

in American Journal of Epidemiology

Published on behalf of Johns Hopkins Bloomberg School of Public Health

Volume 163, issue 1, pages 84-96
Published in print January 2006 | ISSN: 0002-9262
Published online November 2005 | e-ISSN: 1476-6256 | DOI: http://dx.doi.org/10.1093/aje/kwj003
Statistical Issues in Life Course Epidemiology

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There is growing recognition that the risk of many diseases in later life, such as type 2 diabetes or breast cancer, is affected by adult as well as early-life variables, including those operating prior to conception and during the prenatal period. Most of these risk factors are correlated because of common biologic and/or social pathways, while some are intrinsically ordered over time. The study of how they jointly influence later (“distal”) disease outcomes is referred to as life course epidemiology. This area of research raises several issues relevant to the current debate on causal inference in epidemiology. The authors give a brief overview of the main analytical and practical problems and consider a range of modeling approaches, their differences determined by the degree with which associations present (or presumed) among the correlated explanatory variables are explicitly acknowledged. Standard multiple regression (i.e., conditional) models are compared with joint models where more than one outcome is specified. Issues arising from measurement error and missing data are addressed. Examples from two cohorts in the United Kingdom are used to illustrate alternative modeling strategies. The authors conclude that more than one analytical approach should be adopted to gain more insight into the underlying mechanisms.

Keywords: correlation; lifetime; path analysis; regression; structural equation model; G0, grandparents; G1, study participants; G2, study participants' offspring

Journal Article.  7572 words.  Illustrated.

Subjects: Public Health and Epidemiology

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