Updating stochastic networks to integrate cross-sectional and longitudinal studies

Abstract

Clinical trials are typically conducted over a populationwithin adefinedtimeperiodinorder toilluminate certain characteristics ofahealth issue or disease process. These cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modelling detailed prognostic predictions. Longitudinal studies on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, andmany studies only cover a relatively small window within the disease process. This paper explores the application of intelligent data analysis techniques for building reliable models of disease progression from both cross-sectional and longitudinal studies. The aim is to learn disease ‘trajectories’ from cross-sectional data by building realistic trajectories from healthy patients to those with advanced disease. We focus on exploring whether we can ‘calibrate’ models learntfromthese trajectorieswithreal longitudinaldatausingBaum-Welch re-estimation.