894Molecular epidemiology: building causal evidence within highly dimensioned studies

Abstract

Abstract Focus of Presentation This presentation provides a practical perspective on molecular epidemiologic analysis within a single high dimensional study to consider a causal question. Findings The work steps include special issues. Those include composite exposures and understanding of collinearity across predictors. Laboratory artefact needs to be minimised. Non-causal confounding is also important. This can occur when a factor is a determinant of outcome and the underlying association between exposure and the factor is non-causal. That is, the association arises due to chance, confounding or other bias rather than reflecting that exposure and the factor are causally related. The influence of laboratory processing features must be accounted for. This can be achieved by pre-processing measures to remove artefact or controlling for process factors. The work plan should allow the evaluation of alternative non-causal explanations for observed exposure-disease associations and strategies to obtain the highest level of causal inference possible within the study. Conclusions/Implications A systematic approach is required to work through a question a question set and obtain insights on not only the exposure-disease association but the multifactorial causal structure of the underlying data where possible. The appropriate inclusion of molecular findings will enhance the quest to better understand multifactorial disease causation in modern observational epidemiological studies. Key messages A systematic set of work steps is outlined to assess whether detected exposure-disease associations have a low and high likelihood of being attributed to non-causal and causal explanations respectively.