Armitage lecture 2010: Understanding treatment effects: the value of integrating longitudinal data and survival analysis.

There is a single-minded focus on events in survival analysis, and we often ignore longitudinal data that are collected together with the event data. This is due to a lack of methodology but also a result of the artificial distinction between survival and longitudinal data analyses. Understanding the dynamics of such processes is important but has been hampered by a lack of appreciation of the difference between confirmatory and exploratory causal inferences. The latter represents an attempt at elucidating mechanisms by applying mediation analysis to statistical data and will usually be of a more tentative character than a confirmatory analysis. The concept of local independence and the associated graphs are useful. This is related to Granger causality, an important method from econometrics that is generally undervalued by statisticians. This causality concept is different from the counterfactual one since it lacks lacks the intervention aspect. The notion that one can intervene at will in naturally occurring processes, which seems to underly much of modern causal inference, is problematic when studying mediation and mechanisms. It is natural to assume a stochastic process point of view when analyzing dynamic relationships. We present some examples to illustrate this. It is not clear how survival analysis must be developed to handle the complex life-history data that are increasingly being collected today. We give some suggestions.