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.
Bianca De Stavola (London School of Hygiene and Tropical Medicine)
Date & time:
23 Jan 2006 16:00 pm - 23 Jan 2006 00:00 am
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