Clinical trials of fatal diseases often focus on one or more non-fatal events, in addition to survival, both to characterize morbidity and to improve survival estimates. Three statistical complications are that the time to each non-fatal event and subsequent residual survival may be either positivel
A non-parametric competing risks model for manpower planning
β Scribed by McClean, Sally ;Gribbin, Owen
- Publisher
- John Wiley and Sons
- Year
- 1991
- Tongue
- English
- Weight
- 619 KB
- Volume
- 7
- Category
- Article
- ISSN
- 8755-0024
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
In the competing risks problem, a useful quantity is the cumulative incidence function, which is the probability of occurrence by time t for a particular type of failure in the presence of other risks. The estimator of this function as given by Kalbfleisch and Prentice is consistent, and, properly n
Recently, regression analysis of the cumulative incidence function has gained interest in competing risks data analysis, through the model proposed by Fine and Gray (JASA 1999; 94: 496-509). In this note, we point out that inclusion of time-dependent covariates in this model can lead to serious bias
## Abstract A __p__βorder multivariate kernel density model based on kernel density theory has been developed for synthetic generation of multivariate variables. It belongs to a kind of dataβdriven approach and is able to avoid prior assumptions as to the form of probability distribution (normal or
## Abstract The significance level of a nonβparametric linkage (NPL) statistic is often found by simulation since the distribution of the test statistic is complex and unknown. Ideally, simulation occurs by randomly assigning founder genotypes and then simulating meiotic events for the descendants