Data synthesis over a network of multiple treatment comparisons for joint longitudinal and event-time outcomes
Related longitudinal and time-to-event data is often collected from multiple heterogeneous data sources (such as studies, hospitals or centres) to answer clinical questions. Using such data, it is of interest to simultaneously assess all available treatment options, rather than perform multiple pairwise comparisons. However such analyses can be complicated if each data source examines only a subset of available treatment options.
My project will develop methodology linking the areas of joint modelling for longitudinal and time-to-event data and network meta-analyses to allow analysis of such scenarios. The methodology will be implemented in freely available software, which will be streamlined through use of computer science techniques such as Sequential Monte Carlo Samplers. Use of such methods will allow fast fitting of joint models of complex networks of multi-source data, allowing estimations to be efficiently updated based on new information. Analyses of a range of clinical areas will be conducted, including datasets concerning HIV, hypertension, and intensive care unit patients.