Estimating global exposure effect from longitudinal data on multiple surrogates

This is the authors' abstract. We don't add key points for this paper.

This paper develops a methodology for the estimation of the global effect of a treatment from multiple surrogates collected using a longitudinal design. The global effect is modelled as a latent function of time which is allowed to vary for different treatment groups. The latent variable is modelled using low-rank thin plate splines, allowing the shape of the latent profiles to be estimated from data. Identifiability issues are addressed and Bayesian Linear Mixed Model formulation is used which allows for the implementation of WinBUGS. The method is validated through a simulation study and has been implemented on a motivating dataset to measure the effect of exposure to polluted air on lung function.