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In this work we present a method for the statistical analysis of continuallymonitored data arising in a recurrent diseases problem. The model enablesindividual level inference in the presence of time transience and populationheterogeneity. This is achieved by applying Bayesian hierarchical modelling,where marked point processes are used as descriptions of the individual data,with latent variables providing a means of modelling long range dependence andtransience over time. In addition to providing a sound probabilisticformulation of a rather complex data set, the proposed method is alsosuccessful in prediction of future outcomes. Computational difficulties arisingfrom the analytic intractability of this Bayesian model were solved byimplementing the method into the BUGS software and using standard computationalfacilities.  We illustrate this approach by an analysis of a data set on hormonereplacement therapies (HRTs). The data contain, in the form of diaries onbleeding patterns maintained by individual patients, detailed information onhow they responded to different HRTs. The proposed model is able to capture theessential features of these treatments as well as provide realistic individuallevel predictions on the future bleeding patterns.

Modelling and analysis of time in-homogeneous recurrent event processes in a heterogeneous population: A case study of HRTs