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Life Course Epidemiology (LCE)


​​​​Life course epidemiology tries to understand and establish the dynamic character of the association and changes in risk factors and exposures with disease outcome over a life time of a person. Concepts like accumulation of risk, critical and sensitive periods, trajectory and path analysis are important issues.
The focus is on observational longitudinal studies.  A  high level of methodological knowledge and research is required in the field of longitudinal data analysis for studies that cover a large life span of patients or healthy individuals.

Programme Leaders   Mission and Description of the Programme  


The LCE research program seeks to promote Life Course Epidemiology research at the UMCG through the development, improvement and dissemination of relevant advanced methodologies, as well as their applications to the available cohorts at the UMCG. With this focus, the LCE activities are attributable to “prevention” research, which is next to “mechanisms of diseases” and “innovative treatments” one of the research pillars of the overarching  UMCG theme “Healthy Aging”.


We define Life Course Epidemiology as human health research using longitudinal studies with the aim to causally link exposures to long-term outcomes. Social and medical context (e.g. comorbidities) are taken into account. This definition connects also to definitions coined by Ben-Shlomo (2002) and George Davey Smith (2005).

Whereas prevention is the cornerstone of LCE, the program extended its original theme of the investigation of dynamic risk factors and exposures associated with disease occurrence (etiology) with the study of issues that are relevant to patient care, i.e. applied clinical research.  Thereby it encompasses studies based on the disciplines of both public health epidemiology and clinical epidemiology. Whereas in public health epidemiology the ultimate aim is to unravel the complex origins of disease with a clear view to primary prevention, the aim in clinical epidemiology is to enhance the quality of care for patients through investigating mechanisms in the course of the disease, thus contributing to secondary and tertiary prevention.

LCE research is concerned with some specific methodological challenges, which require special attention when it comes to the statistical analysis. Examples are

  • the cluster structure of the data through repeated measurements,
  • prognostic tools considering the variability of predictors over time,
  • evaluating (preventive) interventions/ exposures with lacking randomization, requiring specific methods for causal inference,
  • computational problems due to missing data,
  • high dimensionality of problems, occurring from the improvement of data collection opportunities in recent years (big data),
  • analyses of ecological momentary assessments,

To achieve the aim of our program we strive for methodological improvement with respect to those challenges, through gathering methodological experts from various disciplines, sharing knowledge, experiences and project ideas. The development, improvement and dissemination of advanced methodology is the aim of this program. Consequently, a further goal is  to apply these methods in future research projects to data from our large (population based) cohorts. Examples include LifeLines, Vlaardingen – Vlagtwedde, PREVEND, Pregnancy Anxiety and Depression study, TRAILS, GECKO.


Our ambition herein is to become a center of excellence for Life Course Epidemiology methods, applications and research findings.

Christine zu Eulenburg & Huibert Burger

Relevance to Healthy Ageing  

LCE is the core of Healthy Ageing research in human populations. Following groups of unselected individuals or specific patients over the life span, and investigate their risk factors for incidence of disease and disease complications (or absence of disease) will identify relevant risk factors and predictors for Healthy Ageing. An important aspect of the LCE program is the inclusion of non-clinical, that is societal, research groups. Healthy Ageing is more than the absence of disease. Social capital, informal care, distance to health care facilities and other societal factors contribute to health and well-being at older – but also younger - ages.

An example of LCE’s research projects   

The need of statistical methodology for Life Course Epidemiology
Researcher: Nazanin Nooraee

LCE data can be either continuous or discrete, but a more common type of outcome data is typically of the ordinal form. The different types of outcome variables require different statistical methods. Although these methods have been well developed for continuous and discrete outcomes, they have not been fully developed for ordinal outcomes.
In many applications, multiple ordinal outcomes on each subject are combined into a sum score, also referred to as a scale. These scales are typically analyzed as continuous outcomes, but they frequently do not satisfy the necessary underlying statistical conditions. This approximate analysis may lead to bias in the estimation of the effect of risk factors on disease outcome. The consequence may be that either an irrelevant risk factor is accepted or a relevant risk factor is ignored. An alternative approach to this statistical analysis is finding a set of continuous variables that lies underneath the categorical outcomes. These type of models are referred to as latent variable models and they introduce a one-to-one relationship between continuous variables and categorical outcomes.
A real challenge is modeling the correlation structure of the latent variable. Research in this area is important to LCE, since it may better describe the association of time varying risk factors and disease status measured with ordinal outcomes. One important aspect in the longitudinal data analysis is handling the missing data, since they occur in almost all studies. Reasons for missing data are mistakes in the input of data, non-response to several items from a questionnaire, drop-out of subjects, laboratory errors, and others. Although modern statistical methods have been developed to handle missing data, these methods are not always implemented due to its complexity and lack of standard software. Less sophisticated methods, which have been demonstrated to give inappropriate estimates of the effects, are still popular among researchers. One of the research topics is to investigate what type of missing data approach can be considered best in analyzing longitudinal data when specific conditions on the missing data patterns are satisfied. This area is less developed for longitudinal multivariate ordinal data. Again, this research may lead to better understanding of how the LCE factors influence disease outcomes or quality of life.

Principal Investigators / nr of PhD students