What are Dynamic Predictions
In this post we will explain the concept of dynamic predictions and illustrate how these can be computed using the framework of joint models for longitudinal and survival data, and the R package JMbayes. The type of dynamic predictions we will discuss here are calculated in follow-up studies in which some sample units (e.g., patients) who are followed-up in time provide a set of longitudinal measurements. These longitudinal measurements are expected to be associated to events that the sample units may experience during follow-up (e.g., death, onset of disease, getting a child, dropout from the study, etc.). In this context, we would like to utilize the longitudinal information we have available up to particular time point t to predict the risk of an event after t. For example, for a particular patient we would like to use his available blood values up to year 5 to predict the chance that he will develop a disease before year 7 (i.e., within two years from his last available measurement). The dynamic nature of these predictions stems from the fact that each time we obtain a new a longitudinal measurement we can update the prediction we have previously calculated.
Joint models for longitudinal and survival data have been shown to be a valuable tool for obtaining such predictions. They allow to investigate which features of the longitudinal profiles are most predictive, while appropriately accounting for the complex correlations in the longitudinal measurements.
Fit a Joint Model
For this illustration we will be using the Primary Biliary Cirrhosis (PBC) data set collected by the Mayo Clinic from 1974 to 1984. For our analysis we will consider 312 patients who have been randomized to D-penicillamine and placebo. During follow-up several biomarkers associated with PBC have been collected for these patients. Here we focus …read more
Source:: r-bloggers.com