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The LCE programme aims to provide more insight into and determine the dynamic character of the association and changes in risk factors and exposures with disease outcome throughout life. Concepts such as accumulation of risk, critical and sensitive periods, and trajectory and path analysis are important in this.

The LCE researchers focus on observational longitudinal studies. In-depth methodological knowledge of longitudinal data analyses is required to conduct studies covering the life span of patients and healthy individuals.

The overall aim of the LCE programme is to:

  • Unravel the complex origins of disease, with an eye to primary prevention;
  • Improve the quality of care by investigating mechanisms relevant in the course of disease, thus contributing to secondary and tertiary prevention.
Relevance

Identifying relevant risk factors and predictors for healthy ageing

The LCE activities support prevention research. The LCE team investigates risk factors for disease and disease complications, as well as predisposing factors for absence of disease, by following groups of non-selected individuals or selected patients throughout life (cohorts). Based on these data, the LCE can identify relevant risk factors and predictors for healthy ageing. This unique knowledge offers interesting opportunities for the development of prevention strategies.

The LCE researchers aim to develop, improve, and disseminate advanced methods and use these in future research projects to handle data from large, University Medical Center Groningen (UMCG) population-based, cohorts such as:

  • The researchers intend to promote LCE research at the UMCG through the development, improvement, and dissemination of relevant advanced methodologies, as well as through their applications to the available cohorts at the UMCG.

    To achieve the overall aim of the LCE programme, the researchers focus on methodological improvements to rise to challenges such as:

    • Clustering structure of the data through repeated measurements;
    • Prognostic tools considering the variability of predictors over time;
    • Evaluating actual and preventive interventions/exposures based on lacking randomization;
    • Specific methods for causal inference;
    • Solving computational problems due to missing data;
    • Dealing with high dimensionality problems due to data collection improvements in recent years (big data);
    • Analyses of ecological momentary assessments, necessitating joint methodological expertise from various disciplines;
    • Sharing knowledge, experiences, and project ideas.