stpm2cr: A Stata module for direct likelihood inference on the cause-specific cumulative incidence function within the flexible parametric modeling framework

Sarwar Islam, Paul Lambert and Mark Rutherford
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Sarwar Islam: University of Leicester
Paul Lambert: University of Leicester
Mark Rutherford: University of Leicester

United Kingdom Stata Users' Group Meetings 2016 from Stata Users Group

Abstract: Modeling within competing risks is increasing in prominence as researchers are becoming more interested in real-world probabilities of a patient's risk of dying from a disease while also being at risk of dying from other causes. Interest lies in the cause-specific cumulative incidence function (CIF), which can be calculated by (1) transforming on the cause-specific hazards (CSH) or (2) through its direct relationship with the subdistribution hazards (SDH). We expand on current competing risks methodology within the flexible parametric survival modeling framework and focus on approach (2), which is more useful when we look to questions on prognosis. These can be parameterized through direct likelihood inference on the cause-specific CIF (Jeong and Fine 2006), which offers a number of advantages over the more popular Fine and Gray (1999) modeling approach. Models have also been adapted for cure models using a similar approach described by Andersson et al. (2011) for flexible parametric relative survival models. An estimation command, stpm2cr, has been written in Stata that is used to model all cause-specific CIFs simultaneously. Using SEER data, we compare and contrast our approach with standard methods and show that many useful out-of-sample predictions can be made after fitting a flexible parametric SDH model, for example, CIF ratios and CSH. Alternative link functions may also be incorporated such as the logit link leading to proportional odds models and models can be easily extended for time-dependent effects. We also show that an advantage of our approach is that it is less computationally intensive, which is important, particularly when analyzing larger datasets.

Date: 2016-09-16
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