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When it comes to clinical survival trials, regulatory restrictions usuallyrequire the application of methods that solely utilize baseline covariates andthe intention-to-treat principle. Thereby a lot of potentially usefulinformation is lost, as collection of time-to-event data often goes hand inhand with collection of information on biomarkers and other internaltime-dependent covariates. However, there are tools to incorporate informationfrom repeated measurements in a useful manner that can help to shed more lighton the underlying treatment mechanisms. We consider dynamic path analysis, amodel for mediation analysis in the presence of a time-to-event outcome andtime-dependent covariates to investigate direct and indirect effects in a studyof different lipid lowering treatments in patients with previous myocardialinfarctions. Further, we address the question whether survival in itself mayproduce associations between the treatment and the mediator in dynamic pathanalysis and give an argument that, due to linearity of the assumed additivehazard model, this is not the case. We further elaborate on our view that, whenstudying mediation, we are actually dealing with underlying processes ratherthan single variables measured only once during the study period. This becomesapparent in results from various models applied to the study of lipid loweringtreatments as well as our additionally conducted simulation study, where weclearly observe, that discarding information on repeated measurements can leadto potentially erroneous conclusions.

Dynamic path analysis - A useful tool to investigate mediation processes in clinical survival trials