Why the study?
Does cumulative incidence competing risk analysis provide less biased estimates of cause-specific mortality compared to the Kaplan-Meier method in the presence of competing events?
Does cumulative incidence competing risk analysis provide less biased estimates of cause-specific mortality compared to the Kaplan-Meier method in the presence of competing events?
Cumulative incidence competing risk analysis should be used instead of the Kaplan-Meier method to avoid overestimating cause-specific mortality in the presence of competing events.
Requires cautious interpretation of Kaplan-Meier cause-specific mortality; leaves open routine adoption of competing risk methods in research.
Kaplan-Meier analysis is a popular method used for analysing time-to-event data. In case of competing event analyses such as that of cardiovascular and non-cardiovascular mortality, however, the Kaplan-Meier method profoundly overestimates the cumulative mortality probabilities for each of the separate causes of death. This article provides an introduction to the problem of competing events in Kaplan-Meier analysis. It explains cumulative incidence competing risk analysis and demonstrates on a cohort of elderly dialysis patients that, in contrast to the Kaplan-Meier method, application of this method yields unbiased estimates of the cumulative probabilities for cause-specific mortality.
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Verduijn et al. (2010) studied this question.
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