  In this work we present a method for the statistical analysis of continually
monitored data arising in a recurrent diseases problem. The model enables
individual level inference in the presence of time transience and population
heterogeneity. 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 and
transience over time. In addition to providing a sound probabilistic
formulation of a rather complex data set, the proposed method is also
successful in prediction of future outcomes. Computational difficulties arising
from the analytic intractability of this Bayesian model were solved by
implementing the method into the BUGS software and using standard computational
facilities.
  We illustrate this approach by an analysis of a data set on hormone
replacement therapies (HRTs). The data contain, in the form of diaries on
bleeding patterns maintained by individual patients, detailed information on
how they responded to different HRTs. The proposed model is able to capture the
essential features of these treatments as well as provide realistic individual
level predictions on the future bleeding patterns.
