Many medical studies collect both repeated measures data and survival data. In this talk, I discuss jointly modeling these two kinds of data in a study of patients with chronic kidney disease in which longitudinal biomarkers of kidney function and time to cardiovascular events were recorded. Joint modeling these processes is important because the measurements of kidney function are error-prone. Ignoring this error (e.g., using a simpler time-varying covariates model) can give biased estimates of the effect of kidney function on the risk of cardiovascular events.
I first give an overview of study’s aims and introduce the data. Then I explain the existing tools for joint modeling and show the results of our application of joint modeling to the kidney data. Finally I suggest some extensions of the joint modeling framework that are necessary to better address some key clinical questions.
This is joint work with Peter Diggle and the Salford Royal NHS Foundation Trust.