A comparison of cause-specific and competing risk models to assess risk factors for dementia

Abstract The study of dementia risk factors is complicated by the competing risk of dying. The standard approaches are the cause-specific Cox proportional hazard model with deaths treated as censoring events (and removed from the risk set) and the Fine and Gray sub-distribution hazard model in which those who die remain in the risk set. An alternative approach is to modify the risk set between these extremes. We propose a novel method of doing this based on estimating the time at which the person might have been diagnosed if they had not died using a parametric survival model, and then applying the cause-specific and Fine and Gray models to the modified dataset. We compare these methods using data on dementia from the Australian Longitudinal Study on Women’s Health and discuss the assumptions and limitations of each model. The results from survival models to assess risk factors for dementia varied considerably between the cause-specific model and the models designed to account for competing risks. Therefore, when assessing risk factors in the presence of competing risks it is important to examine results from: the cause-specific model, different models which account for competing risks, and the model which assesses risk factors associated with the competing risk.