Evaluating cardiovascular mortality in type 2 diabetes patients: an analysis based on competing risks Markov Chains and additive regression models

Rationale, aims and objectives Type 2 diabetes represents a condition significantly associated with increased cardiovascular mortality. The aims of the study are: (i) to estimate the cumulative incidence function for cause‐specific mortality using Cox and Aalen model; (ii) to describe how the prediction of cardiovascular or other causes mortality changes for patients with different pattern of covariates; (iii) to show if different statistical methods may give different results. Methods Cox and Aalen additive regression model through the Markov chain approach, are used to estimate the cause‐specific hazard for cardiovascular or other causes mortality in a cohort of 2865 type 2 diabetic patients without insulin treatment. The models are compared in the estimation of the risk of death for patients of different severity. Results For younger patients with a better covariates profile, the Cumulative Incidence Function estimated by Cox and Aalen model was almost the same; for patients with the worst covariates profile, models gave different results: at the end of follow‐up cardiovascular mortality rate estimated by Cox and Aalen model was 0.26 [95% confidence interval (CI) = 0.21–0.31] and 0.14 (95% CI = 0.09–0.18). Conclusions Standard Cox and Aalen model capture the risk process for patients equally well with average profiles of co‐morbidities. The Aalen model, in addition, is shown to be better at identifying cause‐specific risk of death for patients with more severe clinical profiles. This result is relevant in the development of analytic tools for research and resource management within diabetes care.