No abstract

No abstract

No abstract

The distribution M á of the mean à á of a Dirichlet process on the real line, with parameter á, can be characterized as the invariant distribution of a real Markov chain à n . In this paper we prove that, if á has ®nite expectation, the rate of convergence (in total variation) of à n to à á is geometric. Upper bounds on the rate of convergence are found which seem effective, especially in the case where á has a support which is not doubly in®nite. We use this to study an approximation procedure for M á , and evaluate the approximation error in simulating M á using this chain. We include examples for a comparison with some of the existing procedures for approximating M á , and show that the Markov chain approximation compares well with other methods.

No abstract

No abstract

Heart failure (HF) is one of the main causes of morbidity, hospitalization, and death in the western world, and the economic burden associated with HF management is relevant and expected to increase in the future. We consider hospitalization data for HF in the most populated Italian Region, Lombardia. Data were extracted from the administrative data warehouse of the regional healthcare system. The main clinical outcome of interest is time to death and research focus is on investigating how recurrent hospitalizations affect the time to event. The main contribution of the article is to develop a joint model for gap times between consecutive rehospitalizations and survival time. The probability models for the gap times and for the survival outcome share a common patient specific frailty term. Using a flexible Dirichlet process model for %Bayesian nonparametric prior as the random-effects distribution accounts for patient heterogeneity in recurrent event trajectories. Moreover, the joint model allows for dependent censoring of gap times by death or administrative reasons and for the correlations between different gap times for the same individual. It is straightforward to include covariates in the survival and/or recurrence process through the specification of appropriate regression terms. The main advantages of the proposed methodology are wide applicability, ease of interpretation, and efficient computations. Posterior inference is implemented through Markov chain Monte Carlo methods.

Even in chemo-naive patients, cetuximab as single agent is active only in a small fraction of mCRC, similarly to what has been reported for heavily pretreated patients. The extent of benefit when response occurs is, however, such that it is mandatory to intensify the search for the predictive markers of response to cetuximab therapy.