Life course epidemiology is now recognized as an established area in epidemiological research.  One example is the developmental origins of health and disease hypothesis, which tries to show that many chronic adult diseases might have originated in early life.  The ideal of life course epidemiology is to bring in various domains of epidemiological research into investigation of causes of chronic diseases.  To achieve this aim, Life course epidemiology examines hierarchical levels of evidences, such as the inequality of prosperity across countries (higher level), diets and behaviours (individual level) and genetic polymorphism (lower level).

One key research hypothesis is that there may be critical phases of growth, which is crucial to body system development, and impaired growth in these periods may have a long-lasting effect on health in later life.  However, to detect these critical phases are not straightforward, and advanced statistical methodologies have been adopted in recent years to analyse life course epidemiological data.  Each method has its advantages and limitations, and real cohort data sets and computer simulations can be used to compare these methods.

In 2010, I published a paper in Epidemiology (with a commentary by Professor Tim Cole and my reply) on using partial least squares regression (PLSR) to analyse life course data.  The advantage of PLSR over standard generalised linear modelling is that it can deal with the collinearity problem in the life course data caused by the high correlations amongst repeated measures of body size.  A further development of PLSR approach is to transform original body size to relative body size to identify the critical phases of growth related to adverse health outcomes in life course.