The frailty model, an extension of the proportional hazards model, is often used to model clustered survival data. However, some extension of the ordinary frailty model is required when there exist competing risks within a cluster. Under competing risks, the underlying processes affecting the events of interest and competing events could be different but correlated. In this paper, the hierarchi...

PURPOSE Competing risks observations, in which patients are subject to a number of potential failure events, are a feature of most clinical cancer studies. With competing risks, several modeling approaches are available to evaluate the relationship of covariates to cause-specific failures. We discuss the use and interpretation of commonly used competing risks regression models. EXPERIMENTAL D...

The competing risks too arise when one type of event may affect the probability of occurrence of other events. Some authors have made important contributions in this area of research, multistate multivariate models have been proposed. Varieties of record are frequently encountered in areas as medicine, engineering, sociology, biology, social science, among others. Classical survival analysis mo...

Consider a unit which can experience any one of k competing failure types, and suppose that for each unit we observe the time to failure, T , and the type of failure, C ∈ {1, 2, . . . , k}. The case of observing the pair (T, C) is termed “competing risks” in the statistical literature. After considering some examples we review basic notation and theory of competing risks. In particular we consi...

This paper proposes a new bivariate competing risks model that speci…es each marginal duration as the …rst time a Lévy subordinator crosses a random threshold. Our speci…cation is a natural variant of the competing risks version of the mixed proportional hazards model, but it allows time-varying heterogeneity and simultaneous termination of both durations. When the structural multiplicative e¤e...

Competing risks occur commonly in medical research. For example, both treatment-related mortality and disease recurrence are important outcomes of interest and well-known competing risks in cancer research. In the analysis of competing risks data, methods of standard survival analysis such as the Kaplan-Meier method for estimation of cumulative incidence, the log-rank test for comparison of cum...

Barnett and Graves [1], in their commentary on our report recently published in Critical Care [2], suggested that time-discrete methods should be used to address time-dependent risk factors and competing risks. In this letter we comment on two statements by those authors. First, Barnett and Graves claim that, 'An alternative method to the competing risks model is a multistate model.' In fact, a...

Competing risks data usually arises in studies in which the failure of an individual may be classified into one of k (k > 1) mutually exclusive causes of failure. When competing risks are present, there are two main differences with classical survival analysis: (i) survival functions are not mainly used to describe cause-specific failures and, (ii) classical estimation techniques may provide bi...

A resource-efficient approach to making inferences about the distributional properties of the failure times in a competing risks setting is presented. Efficiency is gained by observing recurrences of the competing risks over a random monitoring period. The resulting model is called the recurrent competing risks model (RCRM) and is coupled with two repair strategies whenever the system fails. Ma...