Covariate information in complex event history data - some thoughts arising from a case study
Seminar Room 1, Newton Institute
The motivation behind this talk comes from considering epidemiological follow-up data for the purpose of studying the role of various risk factors of cardiovascular diseases. Commonly in such studies the statistical analysis is based on a hazard regression model where the covariates (e.g. blood pressure, cholesterol level, or body mass index) are measured only at the baseline. In addition to considering such more traditional risk factors, it is becoming increasingly common to try and assess also the role of some genetic factors contributing to the aetiology of such diseases, and then usually restricting the analysis to certain candidate loci that are potentially causative on the basis of the available information about their function. In principle, the corresponding causal mechanisms can involve pathways that are direct in the sense that they influence, in the postulated model structure, directly the outcome variable, or indirect in that their effect on the outcome is mediated via the levels of the measured risk factors.
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