Flexible parametric modelling of cause-specific hazards to estimate cumulative incidence functions
1 Department of Health Sciences, Centre for Biostatistics and Genetic Epidemiology, University of Leicester, Leicester, UK
2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
3 Department of Health Sciences, Biostatistics Group, University of Leicester, Leicester, UK
BMC Medical Research Methodology 2013, 13:13 doi:10.1186/1471-2288-13-13Published: 6 February 2013
Competing risks are a common occurrence in survival analysis. They arise when a patient is at risk of more than one mutually exclusive event, such as death from different causes, and the occurrence of one of these may prevent any other event from ever happening.
There are two main approaches to modelling competing risks: the first is to model the cause-specific hazards and transform these to the cumulative incidence function; the second is to model directly on a transformation of the cumulative incidence function. We focus on the first approach in this paper. This paper advocates the use of the flexible parametric survival model in this competing risk framework.
An illustrative example on the survival of breast cancer patients has shown that the flexible parametric proportional hazards model has almost perfect agreement with the Cox proportional hazards model. However, the large epidemiological data set used here shows clear evidence of non-proportional hazards. The flexible parametric model is able to adequately account for these through the incorporation of time-dependent effects.
A key advantage of using this approach is that smooth estimates of both the cause-specific hazard rates and the cumulative incidence functions can be obtained. It is also relatively easy to incorporate time-dependent effects which are commonly seen in epidemiological studies.