In the presence of competing risks, the estimation of crude cumulative incidence, i.e. the probability of a specific failure as time progresses, has received much attention in the methodological literature. It is possible to estimate crude cumulative incidence starting from models defined on cause-specific hazards or to adopt regression strategies modeling directly the quantity of interest. A generalized linear model based on discrete cause-specific hazard is used to obtain the crude cumulative incidence and its asymptotic variance. The model allows inference both on cause-specific hazard and on crude cumulative incidence in the presence of time dependent effects. Standard software can be used to compute all quantities of interest. A trial of chemoprevention of leukoplakia is considered for illustrative purposes, where different patterns of risk are suspected for the different causes of treatment failure.
ASJC Scopus subject areas
- Computational Mathematics
- Computational Theory and Mathematics
- Statistics and Probability
- Applied Mathematics