Prof. Peter Diggle, University of Lancaster
The choice of approach to the analysis of longitudinal data can, and arguably should, be informed by the purpose of the modelling. At the risk of over-simplifying, a broad distinction can be drawn between whether population-averaged or subject-speciﬁc eﬀects are of primary scientiﬁc interest; see, for example, Diggle, Heagerty, Liang and Zeger (2002). In this talk, I will brieﬂy review some widely used methods of longitudinal data analysis before describing a case-study in renal medicine. Progression towards end-stage renal failure is typiﬁed by an asymptomatic period that can extend over many years, but can be assessed indirectly by bio-chemical analysis of blood-samples. Also, early diagnosis and treatment can materially slow the rate of progression and so postpone the need for expensive and invasive renal replacement therapy (dialysis or transplantation). An important question is therefore: how can we use information that is easily obtained through routine blood-testing to provide early warning of end-stage renal failure? To address this question, I will formulate a dynamic linear model for a large data-set in which several thousand subjects in both primary and secondary care settings have had their renal function measured imperfectly, at irregular sequences of time-points, and use the model in conjunction with a Kalman ﬁlter algorithm to enable real-time updating of the predictive probability that an individual subject’s underlying renal proﬁle has crossed a clinically deﬁned intervention threshold. Finally, I will suggest that statistical modelling has an important role to play in so-called e-health research and give some other examples of related work-in-progress at Lancaster and Liverpool.