Analysing time-to-event data in the presence of competing risks within the flexible parametric modelling framework. What tools are available in Stata, which one to use and when?

Sarwar Islam Mozumder ()
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Sarwar Islam Mozumder: Biostatistics Research Group, Department of Health Sciences, University of Leicester

London Stata Conference 2018 from Stata Users Group

Abstract: In a typical survival analysis, the time to an event of interest is studied. For example, in cancer studies, researchers often wish to analyse a patient’s time to death since diagnosis. Similar applications also exist in economics and engineering. In any case, the event of interest is often not distinguished between different causes. Although this may sometimes be useful, in many situations, this will not paint the entire picture and restricts analysis. More commonly, the event may occur due to different causes, which better reflects real- world scenarios. For instance, if the event of interest is death due to cancer, it is also possible for the patient to die due to other causes. This means that the time at which the patient would have died due to cancer is never observed. These are known as competing causes of death, or competing risks. In a competing risks analysis, interest lies in the cause-specific cumulative incidence function (CIF). This can be calculated by either (1) transforming on (all) cause-specific hazards, or (2) using a direct relationship with the subdistribution hazards. Obtaining cause-specific CIFs within the flexible parametric modelling framework by adopting approach (1) is possible by using the stpm2 post-estimation command, stpm2cif. Alternatively, since competing risks is a special case of a multi-state model, an equivalent model can be fitted using the multistate package. To estimate cause-specific CIFs using approach (2), stpm2 can be used by applying time-dependent censoring weights which are calculated on restructured data using stcrprep. The above methods involve some form of data augmentation. Instead, estimation on individual-level data may be preferred due to computational advantages. This is possible using either approach, (1) or (2), with stpm2cr. In this talk, an overview of these various tools are provided followed by some discussion on which of these to use and when.

Date: 2018-10-15
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