Abstract
BackgroundWhen a patient experiences an event other than the one of interest in the study, usually the probability of experiencing the event of interest is altered. By contrast, disease-free survival time analysis by standard methods, such as the Kaplan-Meier method and the standard Cox model, does not distinguish different causes in the presence of competing risks. Alternative approaches use the cumulative incidence estimator by the Cox models on cause-specific and on subdistribution hazards models. We applied cause-specific and subdistribution hazards models to a diabetes dataset with two competing risks (end-stage renal disease (ESRD) or death without ESRD) to measure the relative effects of covariates and cumulative incidence functions.ResultsIn this study, the cumulative incidence curve of the risk of ESRD by the cause-specific hazards model was revealed to be higher than the curves generated by the subdistribution hazards model. However, the cumulative incidence curves of risk of death without ESRD based on those three models were very similar.ConclusionsIn analysis of competing risk data, it is important to present both the results of the event of interest and the results of competing risks. We recommend using either the cause-specific hazards model or the subdistribution hazards model for a dominant risk. However, for a minor risk, we do not recommend the subdistribution hazards model and a cause-specific hazards model is more appropriate. Focusing the interpretation on one or a few causes and ignoring the other causes is always associated with a risk of overlooking important features which may influence our interpretation.
Highlights
When a patient experiences an event other than the one of interest in the study, usually the probability of experiencing the event of interest is altered
In a study of people with diabetes, end-stage renal disease (ESRD) and death compete for the life of the person, and each influence the risk of the other [3,4]
In this study we explored and determined the effect of diabetes on ESRD and death when demographic characteristics were taken into account in the competing risks analysis
Summary
When a patient experiences an event other than the one of interest in the study, usually the probability of experiencing the event of interest is altered. Alternative approaches use the cumulative incidence estimator by the Cox models on cause-specific and on subdistribution hazards models. We applied cause-specific and subdistribution hazards models to a diabetes dataset with two competing risks (end-stage renal disease (ESRD) or death without ESRD) to measure the relative effects of covariates and cumulative incidence functions. Each person studied can experience one of several different types of events over the followup period and survival times are subject to competing risks if the occurrence of one event type prevents other event types from occurring. In a study of people with diabetes, end-stage renal disease (ESRD) and death compete for the life of the person, and each influence the risk of the other [3,4].
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